research paper on industry innovation and infrastructure

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Goals of sustainable infrastructure, industry, and innovation: a review and future agenda for research

Sanjeet singh.

University Centre for Research & Development & Department of Management Studies, Chandigarh University, Gharuan, Mohali, Punjab India 140413

Associated Data

The data used for analysis is available on the Scopus.

Sustainable Development Goal 9 targets (SDG 9 targets) are mainly tracked through the indicators of penetration of internet and mobile broadband subscription, logistic performance index, quality and ranking of the universities, investment in research and development initiatives, industrial reforms and emission control, and connectivity to rural areas. The attainment of many of these targets and tracking of indicators is confronted by challenges of poor awareness, funding issues, distorted policies, and implementation failures. This systematic review on achievements, challenges, and future scope in attaining SDG 9 consolidates the literature from the Web of Science, related to SDG 9 and indicators, since 2017; develops bibliometric patterns; conducts thematic analysis by focusing the leading indicators of SDG 9; and develops agenda for future research. The major limitations of this study include focusing on selected indicators and limited literature availability. This review recommends policymakers, researchers, and administrators to focus on promising themes such as tackling the digital divide and ensuring digital justice and digital equality; clean fuel and technology adoption; enhancing internet and mobile broadband subscription with reduced negative impacts, logistic sector reforms; industrial policy reforms and technology integration; improving the quality and sustainability of universities; and increasing funding and support for research and development initiatives and improving the rural connectivity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11356-023-25281-5.

Introduction

Infrastructure, sustainable industrialization, and innovation is the key to achieving Sustainable Development Goal 9 (SDG 9). The SDG 9 can be tracked with eight targets and twelve indicators developed by the United Nations (United Nations 2021 ): road access to rural population, passenger and freight volume by mode of transport, increasing the share of manufacturing in GDP and employment, increasing share small scale industries, medium and hi-tech industries in total industry value-added, carbon dioxide emissions for economic value-added, expenditure on research and development as a percentage of GDP, the share of researchers among inhabitants, international support to infrastructure, and the population covered by the mobile networks and internet access (SDG Tracker 2021 ; United Nations 2021 ). The UN updates of these targets show that 57.7% of the rural dwellers lack good access to the road in twenty-five countries, and COVID-19 impact is severe on the targets of SDG 9, with poor numbers from manufacturing and employment manufacturing and weak air travel demand. Imposing restrictions on the exploitation of resources; monitoring, and controlling emissions, enhancing urban public infrastructure and public services, rationally planned industrial development; and reducing dependence on fossil fuels are essential for attaining sustainability goals (Zhao et al. 2021 ). The impact of carbon emissions is causing severe challenges in the attainment of the SDG 9 targets by 2030, and policy actions, green energy consumption, and successful migration to clean technologies are inevitable to attain these targets (Akhtar et al. 2022 ). Similarly, supporting tourism and innovation in industries and infrastructure development are also essential for attaining the targets of SDG 9 (Anser et al. 2021 ). However, there is increased spending in research and development and progress in the number of researchers among inhabitants (United Nations 2021 ). Moreover, the sustainable development goals of industry, innovation, and infrastructure (SDG 9) are highly connected with other SDGs (Mantlana and Maoela 2020 ) (Coenen et al. 2021 ) and climate actions (Coenen et al. 2021 ).

In Asia, six countries have internet penetration below 25%, and thirteen countries are in the 25–50% level of internet penetration. Eight Asian countries are with internet penetration between 50 and 75% and fourteen countries with internet penetration above 75%. The mobile broadband penetration of twelve Asian countries is below 50%, and the remaining countries have achieved mobile broadband penetration above 50%. According to the logistic performance index (measured on a scale of 1–5), twenty-seven Asian countries are with logistic performance index below 3, and fourteen countries are with logistic performance index above 3. Fourteen Asian countries have a “Times Higher Education Universities Ranking” (average score of top three universities) zero, twenty-four Asian countries have an average score below fifty, and four countries have an average score above fifty. Only seven Asian countries are spending more than 1% of their GDP on research and development (Sachs et al. 2021 ).

Thirty-two African countries have internet penetration below 25%, and eleven countries are in the 25–50% bracket and 50–75% each. Eighteen African countries are with mobile broadband penetration above 50%, and thirty-six countries are with mobile broadband penetration below 50%. Only two African countries are with logistic performance index above three. Only one African country is with an average score of the top three universities above fifty, and none of the African countries are spending at least 1% of their GDP for research and development initiatives (Sachs et al. 2021 ).

All countries in Europe are having internet penetration above 50%, and twenty-eight countries are with internet penetration above 75%. Similarly, two European countries are with mobile broadband penetration below 50%. Only thirteen European countries are with a Logistic performance index below three. Thirteen European countries with an average score of top three universities above fifty, and fourteen European countries are spending less than 1% of their GDP for research and development (Sachs et al. 2021 ). Sustainable infrastructure and sustainable industries are very essential for the achievement of SDG 9. The research development, sustainable transport, and innovations are the focus areas for the SDG 9 targets in the European context (Bere-Semeredi and Mocan 2019 ).

In the Oceanic region, two countries are with internet penetration above 75%, and two countries have internet penetration levels between 50 and 75%. Three countries have poor penetration of the internet, below 25%, and seven countries are in the 25–50% bracket. Nine countries from the Oceanic region are with mobile broadband penetration below 50%. There are two countries with the logistic performance index above three. Two Oceanic countries with an average score of the top three universities above fifty and only two Oceanic countries are spending more than 1% of their GDP for research and development (Sachs et al. 2021 ).

In North America, all three countries have internet penetration above 70% and mobile broadband penetration above 75%. The logistic performance index of all countries except one is above three. Similarly, except for one country, the average score of the top three universities is above fifty, and the spending on research and development is above 1% of the GDP figures (Sachs et al. 2021 ). In South America, one country has internet penetration below 25%, three countries are in the 25–50% bracket, and seven countries are in the 50–75% level of achievement. Two countries are with mobile broadband penetration below 50%. Only one South American country is with logistic performance index above three. None of the South American countries is with an average score of the top three universities above fifty. Similarly, the only country in the region is spending at least 1% of its GDP on research and development initiatives (Sachs et al. 2021 ).

In the Caribbean region, one country is with internet penetration below 25%, and five countries are in the 25–50% bracket. Eight Caribbean countries are in the 50–75% bracket in respect of internet penetration, and three countries are with internet penetration above 75%. Eight countries from the Caribbean region are with mobile broadband penetration below 50%. None of the Caribbean countries are with logistic performance index above three. Similarly, no country in the Caribbean region is with an average score of the top three universities above fifty. None of the Caribbean countries are spending more than 1% of their GDP on research and development (Sachs et al. 2021 ).

High mobile broadband penetration and internet penetration of the country show the progress of the country in digital infrastructure and the level of the digital-savvy population. The higher ranking of universities shows the quality of infrastructure in education. In short, higher numbers are preferred in all these three indicators of SDG 9. Figures  1 and ​ and2 2 are based on the Sustainable Development Report 2021, by Cambridge University Press, and visualized in this review using Excel tools. Figure  1 visualizes the internet penetration, mobile broad brand subscription, and the performance of leading universities among leading economies of the world. The internet penetration among the leading countries is at above 80% level in leading economies other than China. China has a huge population and its no one country in the world and that can be the reason for a comparatively lower level of internet penetration. All the leading economies have high-level penetration of mobile broad brands; Japan and the USA are much ahead of other leading economies. Based on the average score of the top three universities, the USA and the UK are the regions with top universities, and Italy is comparatively lagging in performance in this indicator of SDG 9.

Selected SDG 9 indicators of leading economies. Data source: Sustainable Development Report 2021, by Cambridge University Press

A higher level of spending for research and development as a percentage of the GDP and, similarly, a higher number in the logistic performance index on a scale from 1–5 are desirable. Figure  2 is the continuation of Fig.  1 and tracks the performance of the leading economies in the world in respect of their expenditure on research and development as a percentage of the Gross Domestic Product and the logistic performance index on a scale of 1–5. Japan is the country with the highest spending for research and development, as a percentage of GDP. Japan spends about 3.3% of its GDP followed by Germany and the USA. Italy, comparatively spending less in this respect, spends only 1.4% of the GDP for research and development initiatives. The logistic performance index of Germany is the best in the group, followed by Japan. The logistic performance of all the countries in the group is more than satisfactory level.

Industry, innovation, and infrastructure are the three basic pillars for achieving SDG 9, and the industry-related targets of SDG 9 can be better monitored by an index on SDG 9 indicators on industry-based indicators. In the study on the industry-based index related to SDG 9, the industrially developed countries (Ireland, Germany, the Republic of Korea, Switzerland, and Japan) are much ahead of the remaining countries (Kynclova et al. 2020 ), and similarly, the index for monitoring the SDG 9 targets is an index based on five indicators related to three industry-related targets (Saieed et al. 2021 ). Better policy implementation and policy reforms can be possible through the analysis comprehensive evaluation index for SDG 9-related targets.

This paper is with six chapters: an introduction section of SDG 9 with a global focus is the first chapter, followed by a review methodology of the paper. Bibliometrics is in the third and fourth sections which are thematic discussions. The recommendations and agenda for future research are the fifth section, and the concluding section is the last chapter. This paper on the industry, infrastructure, and innovation in the global scenario discusses the role of internet penetration, mobile internet, ranking of the top universities, logistic performance, and the government spending in research for achieving targets of SDG 9, i.e., for the goals of sustainable industries and sustainable infrastructure.

Research objectives

• To summarize the SDG 9 initiatives and achievements at a global level
• To consolidate the literature on SDG 9
• To find out future research niches on SDG 9

Research questions

• What challenges are faced in the achievement of SDG 9 targets?
• What can be done for achieving the targets of SDG 9?
• What are the achievements of SDG 9?
• Which are the promising areas for future research?

Review methodology

Web of Science is a professional database for academic records comprising highly impactful journals and conference proceedings. It covers more than two hundred and fifty disciplines and has a huge collection on the topics of sustainability, sustainable development goals, sustainable industry, infrastructure, and innovation. There are high-quality academic papers based on the single-source model (Lage Junior and Godinho Filho 2010 ; Jabbour 2013 ; Talan and Sharma 2019 ). This paper adopted the single-source model (only Web of Science resources). This paper has used the goal-specific keywords “Sustainable Development Goal 9 “ and “SDG 9” and indicator-specific keywords “internet penetration,” “logistic performance index,” “mobile broadband subscription,” “Times University Ranks,” “Scientific and technical journal articles (per 1000 population),” and “Expenditure on research and development” (% of GDP) which were used on 19/12/2021 and drew 204 papers which were obtained for the review on topic-based research. However, thematic analysis is conducted only on four indicators, as no suitable publication is obtained on the last two indicators. This paper followed paper selection based on Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (refer to Fig.  3 ). PRISMA guidelines is used for introducing better transparency in the systematic review. It includes the minimum set of disclosures/reporting items in systematic reviews and meta-analyses. PRISMA guidelines facilitate a better quality analysis of the systematic review/meta-analysis and also help in replicating the study. PRISMA guidelines provide a detailed checklist for preparing a systematic review/meta-analysis (including 27 checklist items). The checklist covers guidelines for the preparation of the title, abstract, introduction, method, result discussion, and funding.

Paper identification and screening process. Data source: Web of Science database

In the first step of paper selection, the 204 documents are checked for anonymous records and duplicates. In this step, all papers are passed to the next stage for screening, as there are no duplicates and anonymous records. The 204 documents are checked for eligibility to include in this paper. A total of 164 documents are removed after reading the title and abstract due to irrelevance to the topic in discussion or without an abstract. In the final step, forty papers are selected for thematic discussion. This paper was motivated by the structure and model adopted in the works (Bansal et al. 2019 ; Jain et al. 2019 ; Srivastava et al. 2020 ).

This paper has used two filters or exclusion criteria for article selection. All publications before 2016 have been avoided from thematic analysis. Similarly, non-English publications are also avoided.

Bibliometric results

Bibliometric analysis is performed on the search result from the Web of Science. The major research parameters like the H index, M index, and G index are used for measuring the impact of document sources and authors. H index value one shows that one publication has received one citation. M index, a derivative of the H index, analyzes the H index on yearly basis from the first publication. The G index number represents the top g articles received together with at least g 2 citations. The figures used in the bibliometric analysis (Figs.  4 , ​ ,5, 5 , ​ ,6, 6 , ​ ,7, 7 , and ​ and8) 8 ) are based on the bibliometric details of articles downloaded from the Web of Science database, and results are visualized using the “Biblioshiny.”

Journal impact analysis. Data source: Web of Science database

Author analysis. Data source: Web of Science database

Country analysis. Data source: Web of Science database

Country collaboration. Data source: Web of Science database

Keyword analysis. Data source: Web of Science database

Journal analysis

The most influential journals related to research on SDG 9 are “ Sustainability” (14 citations), “Journal of Cleaner Production” (234 citations), “Applied Energy” (20 citations), “ Environmental Science and Pollution Research”(4 citations), and “International Journal of Sustainable Development and World Ecology” (22 citations). The detailed analysis of document sources mentioned above is shown in Fig.  4 . “Sustainability” leads in document production, with eight publications followed by “Journal of Cleaner Production” (seven publications) and “Applied Energy” (six publications). In all three research quality parameters, the H index, the M index, and the G index, the “Journal of Cleaner Production” has a leading role. “Sustainability” and “Applied Energy” have a similar impact on all these parameters and closely follow the “Journal of Cleaner Production.”

“Sustainability” published articles on the topics related to Logistics 4.0 of Brazil’s industry and its relation with SDG 9, digital infrastructure assessment tools in line with SDG 9, impacts of the production of woody pellets and SDG 9, usage and applications between artificial intelligence and SDGs including SDG 9, the role of the construction industry in the achievement of SDG 9 targets, research and development and sustainable economic performance, and sustainable e-tourism. “Journal of Cleaner Production” published on technological innovation and environmental quality, sustainability implications of the carbon emissions, circular supplier selection in the petroleum industry, innovations in sustainable supply chains, energy consumption and urbanization, and bio-economy-related SDGs. “Applied Energy” published articles on measuring the achievement of SDG 9 targets, tracking the progress on SDG 9, and inclusive and sustainable industrial development in China. The article published in “Environmental Science and Pollution Research” is related to pavement maintenance management of roads and the relation between international tourism and environmental degradation. “International Journal of Sustainable Development and World Ecology” published articles related to economic activities and SDGs and business models towards SDGs.

Analysis of authors

The leading authors related to SDG 9 are Sinha A, Quatraro F, Ranjbari M, Esfandabadi ZS, and Siebers PO. The detailed analysis of authors on leading parameters is compared in Fig.  5 . Sinha A leads in the H index and G index value and is the influential author on this topic. Quatraro F, Ranjbari M, Esfandabadi ZS, and Siebers PO have similar impacts on this topic, especially in the case of the H index and G index. All the top five authors of this topic have similar M index (refer to Fig.  5 ).

Sinha A leads the domain with three publications and a total of 127 citations. The major works are related to technological innovations and sustainable development goals including SDG 9. The articles Chen et al. 2021 ) and (Sinha et al. 2020b ) are published on “Technological Forecasting and Social Change”; (Sinha et al. 2020a ) is published in “Journal of Cleaner Production.” The collaboration among Quantraro F (University of Turin), Rajbari M (University of Turin), Esfandabadi ZS (Polytechnic University of Turin, Italy), and Siebers PO (University of Turin) has written two articles related to SDG 9, (Ranjbari et al. 2021a , b ), published in “Environmental Development and Sustainability,” and (Ranjbari et al. 2021b ) published in “The total citations for these articles is thirty-nine. The articles dealt with sustainable development goals and COVID 19.”

Analysis of countries

The region-wise analysis related to SDG 9 and the bibliometric details is given in Fig.  6 . The collaborations based on countries are given in Fig.  7 . The dark blue color indicates leadership in research, the red lines are used for showing collaborations, and the thickness of the red lines indicates the strength of research relation between countries. The leading countries of this research domain are China, Germany, Brazil, India, and the UK. China has strong research collaborations with the USA, the UK, Italy, Malaysia, Denmark, and Iran. Germany has strong research connections in this topic with the UK, Brazil, and the USA. The other countries with active collaborations include the UK and Italy.

China leads in document publications with nine publications and one hundred and two citations. China is followed by Germany in document publications (five publications) and India in citations (one hundred and one citations) (refer to Fig.  6 ).

Keyword analysis

The keyword analysis was shown in Fig.  8 . Management, system, big data, climate change, conservation, consumption, and impact are the most occurred keywords in the documents selected for thematic analysis.

Thematic analysis

The sustainable development goals of industry, infrastructure, and innovation focus on six major indicators for the monitoring of performance and for reframing strategies and policies. However, the thematic framework of this paper is based on four of these indicators, and two indicators are avoided due to the unavailability of published documents directly related to this topic. The indicators to be monitored for the achievement of SDG 9 targets are the internet penetration levels, logistic performance index published by the World Bank, percentage of mobile broadband subscription, and the average score of the top three universities in the Times Higher Education University Ranking (Fig.  9 ).

Key themes, sub-themes

Internet penetration

Sustainable Development Goal 9 deals with industry, innovation, and infrastructure. The internet infrastructure and internet penetration of the country can play a big role in communication, education, entertainment, and other industries. Internet penetration has casual relations with economic growth, and thus, rapid internet penetration is key for economic growth (Pradhan et al. 2016 )(Harb 2017 )(Haini 2019 ); for promoting economic growth, especially farmers’ income (Li 2020 ) and regional financial development (Jiang et al. 2020 ); and for stock market efficiency (Afshan et al. 2021 ). However, the contradictory results were obtained on the results on the role of internet penetration on income and environmental actions (Zhang and Meng 2019 ) and inclusive financial development and growth in per capita income (Song et al. 2017 ). The internet penetration itself is dependent upon income, education, demographics, telephone and broadband subscriptions, computer and mobile phone ownership, networks, political environment and media (Vincent 2016 ), tariff, and competition policy (Lange 2017 ). Even though internet access can bring business growth and employment, it can also enhance the digital divide (Latapu et al. 2018 ). The digital divide in Sub-Saharan Africa is due to the spillover effect, GDP, per capita income levels, politics, regulations, population, and electricity infrastructure of the country (Myovella et al. 2021 ).

However, the impact of internet penetration in income inequality in developing regions is severe in comparison with developed regions in Asia (Panichsombat 2016 ); there are also studies pointing out the reduction in inequality due to enhanced internet penetration (S A Asongu and Odhiambo 2019a , b ). Industrial intensity and internet penetration are related and are substitutes. Industrial penetration can reduce the total expenditure on internet intensity (Chang et al. 2018 ). Internet penetration also promotes Internet Financial Reporting (Ariff et al. 2018 ). Internet penetration can also promote sustainable consumption (Wang and Hao 2018 ) and innovations (Xiong et al. 2021 ); international tourism (Vanesa Lorente-Bayona et al. 2021 )(Lee et al. 2021 ), but subject to forex regulations (Vanesa Lorente-Bayona et al. 2021 ); higher education (Mousa and Elamir 2019 ) and school education (Asongu 2020 )(Mate et al. 2020 ); for gender economic inclusion (Asongu and Odhiambo 2020 ); promote insurance penetration and governance (Asongu et al. 2020 ) and government’s environmental protection expenditure (Zhang et al. 2022 ).

Mobile broadband subscription

Mobile broad brand subscription rates are increasing and improving energy efficiency, promoting environmental sustainability reducing carbon emissions (Zhao et al. 2021 ), and promoting international tourism (Kumar and Kumar 2020 ). The mobile broadband penetration can be in three phases take-off, fast-diffusion, and saturation (Teklemariam and Kwon 2020 ). Mobile broadband subscriptions are saturating in many developed countries (Bento 2016 ). They have diversified applications and huge potential for exploring the opportunities for e-governance (Kyem 2016 ). Moreover, it is not only the mobile broadband subscriptions but, quality, i.e., the speed of networks is also equally important (Abeliansky and Hilbert 2017 ). Mobile broadband penetration can be a determinant for enhancing subscriptions to life and non-life insurance policies (Simplice A Asongu and Odhiambo 2019a , b ).

Times Higher Education University Ranking

University ranking is an important factor in higher education. Times Higher Education University Ranking analysis is one of the authenticated rankings of international universities, pointing out that size and internationalization are important determinants of ranking (McAleer et al. 2019 ), and shows different ranking performance by technical universities. Technical universities outshine in industrial income and lags in research and teaching (Perez-Esparrells and Orduna-Malea 2018 ).

Logistics performance index

Logistics has a significant role in the economic growth of modern trade and commerce. In a globalized world, the logistic efficiency and performance of the country are to be monitored for the development of industry and infrastructure. The logistic performance index monitors the country’s logistic ability (Zekic et al. 2017 ) and is based on six indicators. Among various factors affecting the logistics performance index, the GDP per capita, the percentage of commercial service imports, and the liner shipping connectivity index are very important and have a negative relation with commercial transport service imports (Alnipak et al. 2021 ), reliability of supply chains, and the predictability of service delivery for producers and exporters (Kampf et al. 2016 ).

There are several challenges associated with attaining the targets of SDG 9. Road access for the rural population is still a long away from targets, and heavy investments are required in providing road access in rural areas. The targets of increased passenger and freight volume and share of the small, medium, and hi-tech businesses are severely strangled by the COVID pandemic. Even in these recovery periods from the pandemic, the volumes need to pick up for achieving the SDG 9 targets. Similarly, the achievements related to the reduction of carbon emissions are still far away from sustainability goals. However, positive signs are visibly related to the SDG 9 targets associated with research and development and the penetration of mobile networks and internet access. In mapping, in the literature related to indicators associated with SDG 9 and papers directly related to SDG 9, very few papers have worked on challenges and solutions associated with achieving internet penetration and mobile network access, except a few documents on the need for improving speed and quality of the network. Similarly, very little literature has directly dealt with connections between rural road access and the attainment of SDG 9 targets. Internationalization, size, generation of industrial income, innovation, research funding, teaching quality, and research orientation are the key points to be focused on for achieving targets related to research and SDG 9. Very few research works are available on the logistic performance index and SDG 9 targets, passenger traffic volume, and SDG 9. These are promising research gaps, and the following section deals with research niches for further research.

Future research agenda

Thematic recommendations

The major themes for future research related to SDG 9 can be on the various dimensions and impacts of internet penetration and mobile broadband subscriptions. The industrialization of various levels, impacts on employment and income, and the issues of technology integration and innovations are also promising themes for further research. The sustainability initiatives based on improving road access, measures for improving the rankings of universities, and strategies for improving the research and research fundings can also research themes for future research. The logistic performance index and related measures are also promising areas for future research (Fig.  10 ).

Methodological propositions

The literature review on SDG 9 has identified the conclusions that internet penetration cannot result in positive impacts in income, per capita income, inclusive financial development, and environmental actions. Empirical research can be conducted for further exploration of the relations and confirming the existing findings. If the negative impact is confirmed, the reasons and determinants for the negative impacts of internet penetration should be researched scientifically. Similarly, comparative studies based on multiple countries and demographies should also be tested to get a comprehensive picture of the socio-economic impacts of internet penetration. The digital divide is another area, that is, to be explored are solutions for the digital divide, and research can also be for ensuring digital justice and measures for improving digital literacy and digital access for all. Research can also be for documenting these issues in the countries with high internet penetration and the strategies for coping with these challenges. Such studies should explore the policy changes and reforms essential for improving internet penetration and removing the challenges. Initiatives can be taken up by the researchers for designing measures for innovative funding sources for the construction and maintenance of roads which can be a boost for connectivity and logistics. The appraisal of universities with global benchmarks and spotting out the quality issues and constraints can be a big step towards improving the quality of universities and helpful for improving university ranking at the global level. Research can also be for improving incomes and funding for universities. Awareness programs and training for removing the digital divide and improving the internet penetration and mobile broadband subscriptions are another area that can be taken up for further research. Reviews can be conducted on the constraints on various dimensions of industrial policies, which can spot out challenges and can be helpful for policy reforms. Bibliometric analysis can be considered on each research theme identified in “ Thematic recommendations .”

Policy recommendations

Several positive impacts of the internet penetrations discussed in the literature have only regional confirmation, and further research on the global level can be more meaningful and provides more insights for policy and implementation levels. Better schemes at government and semi-government levels can be made for better connectivity to rural areas by all-weather roads and maintenance on an urgent basis. Fundings and multiple-level sanctioning should not be a constraint for such measures of infrastructural developments. Regulatory modifications can be planned for improving internet penetration and mobile broadband access, and measures can be taken to improve digital awareness, access, and affordability. Industry policy reforms with a focus on the environment, economy, and sustainability are inevitable. Reforms should be made without job losses, and for a smooth transition to more energy and climate, efficient technologies should be prioritized. More government funding and international funding including options of climate finance can be considered while developing policies and strategies for sustainable industrial reforms. This paper also recommends reforming the research and development sector, by providing automation and self-sufficiency for universities, quality improvement measures, better funding opportunities, and other measures for quality enhancement, research output, and ranking of the universities. Policy measures can also be initiated for improving the logistic performance index at country levels.

Conclusions

The targets of SDG 9 focus on enhanced road connectivity in rural areas, industrial reforms, improved logistic infrastructure, higher level of internet penetration and subscription of mobile broadband connections, increased spending for research and development, and improving the quality and ranking of universities. This review looks into addressing the achievements, determinants, and challenges associated with these targets and indicators of SDG 9. Many of the targets and indicators associated with SDG 9 are under-researched and offer promising themes for academicians, researchers, and scholars for further research. The issues discussed in this review are important for policymakers and administrators for developing and reforming policies to better address challenges associated with the attainment of SDG 9 targets.

Internet penetration in society is influenced by income, education, demographics, telephone and broadband subscriptions, computer and mobile phone ownership, networks, political environment, media, forex regulations, tariffs, and competition policy. Internet penetration has several proven advantages including its positive role in economic growth, regional financial development, stock market efficiency, and international tourism. The major concerns of internet penetration include its role in income and environmental actions, inclusive financial development, and the digital divide. Eighty-seven countries of the world are with internet penetration below 50%, where fourteen of them are from Asia, and forty-three are from Africa. These are the regions that need urgent action for digital awareness, training, and education. Huge investments are required for building digital infrastructure and connectivity. Policymakers and administrators should also focus on the next thirty-six countries with internet access in the range of 50–75%. The majority of the countries in this bracket are from Africa, Asia, and South America.

Along with internet penetration, the mobile broadband subscription trend is another leading target and indicator of SDG 9. Higher rates of mobile broad brand subscriptions have positive impacts on energy efficiency, environmental sustainability, and international tourism and have a big role in reducing carbon emissions. Fresh policy initiatives and reforms for enhancing mobile broadband subscriptions in under-penetrated areas need urgent priority. Funding and technology challenges need to be addressed to tackle this issue, and the below 50% subscription of mobile broadbands is concentrated on twelve Asian, nine Oceanic, two European, thirty-six African, and two South American countries.

Performance in the logistic performance index is crucial for the attainment of targets of SDG 9. The Asian and African regions need fresh projects, funds, and technology for improving their logistic infrastructure. Researchers should also focus on the development of industry and infrastructure, the GDP per capita level, the percentage of commercial service imports, the liner shipping connectivity index, the commercial transport service imports, and the reliability and predictability of logistic stakeholders for improving logistic performance. Fresh research is essential for removing various constraints in these regions for addressing funding challenges, repayment issues, etc.

The fourth pillar for better achievement of SDG 9 is related to spending on research and development and ranking of universities. Very few non-European countries spend more than 1% of their GDP on research and development. This paper recommends increased funding for research and development, promotion of collaborated research, and academic tie-ups in this regard. Educational reforms are the key to the rapid attainment of such goals. Equally important is the quality improvement of higher education institutions. The Times Higher Education Universities Ranking is the popular indicator in this regard, and size, internationalization, funding, income generation, and quality are the major factors affecting the university’s ranking and performance. This paper recommends developing diversified income streams for universities, along with strong policy measures for ranking improvement, quality education, and research outputs.

This paper recommends research themes for further research. These themes can encourage fresh research and also helps in obtaining sponsorship for better achievement of targets related to SDG 9. The promising themes can be the negative impacts of internet penetration and mobile broadband subscription, challenges and solutions, and policies, technology, and funding solutions for a higher level of internet penetration and mobile broadband subscription. The impact of internet penetration and mobile broadband subscription in education, the economy, society, and the environment can also bring positive outcomes for achieving the targets related to SDG 9. Future researchers can also focus on the digital divide, digital access, and affordability of digital technologies as also the security challenges and misuses. This paper strongly recommends digital justice and equality including gender equality and fairness in digital space. Improved connectivity and logistical infrastructure are other propositions for further research. The topics for improving educational ranking and quality improvement include research automation, income, funding of universities, benchmarking, accreditations, research, publications, affiliations, collaborations, industrial tie-ups, and scientific appraisal systems. This paper also recommends reviews on the constraints of various dimensions of industrial policies, for identifying challenges and policy reforms. The researchers can focus on two indicators untouched in the existing literature including rural road access, spending on research and development from GDP, and measures for increased research outputs.

This paper is subjected to limitations with very little extensive research in the literature related to individual indicators and due to limitation in literature and data availability, all indicators of SDG 9 is not analyzed, and the research is limited to the four important indicators. The constraints of secondary data are another limitation. Research funding is another challenge in this topic. Among the literature on this topic, only 56% of the publications are funded. Future researchers can focus on the leading sponsors for funding their projects. “National Council for Scientific and Technological Development” of Brazil and “Coordenação de Aperfeiçoamento de Pessoal de Nível Superior” or “CAPES Foundation” of Brazil are the leading sponsors in the research related to SDG 9, with three funded projects each. The sponsored research by the National Council for Scientific and Technological Development and CAPES Foundation has got forty-one citations, and the topics were related to business models towards SDGs and barriers and combined usage of circular economy and industry 4.0 for achieving SDGs including SDG 9. National Council for Scientific and Technological Development also funded the research on Logistics 4.0 in Brazil and the achievement of SDG 9 targets. CAPES Foundation has been funded individually for the research on smart practices in the Higher Education Institutions at Brazilian Universities. The other leading fund sponsors of SDG 9 are the Chinese Academy of Sciences, the European Commission, and the National Natural Science Foundation of China.

Below is the link to the electronic supplementary material.

Author contribution

Sanjeet Singh conceptualize the idea, collected the data, and did the analysis. Jayaram wrote the literature review and the findings of the manuscript.

Data availability

Declarations.

Ethical approval is not required as there is no human data or primary data involved in the manuscript.

Not applicable, and no human or individual is involved.

The authors declare no competing interests.

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Gill, J.C., Dahal, R.K., Smith, M. (2021). Infrastructure, Industry, and Innovation. In: Gill, J.C., Smith, M. (eds) Geosciences and the Sustainable Development Goals. Sustainable Development Goals Series. Springer, Cham. https://doi.org/10.1007/978-3-030-38815-7_9

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The challenges of the COVID-19 pandemic and building back better highlight the importance of a long-term strategy for industrialization, innovation, digitalization and the creation of resilient infrastructure. This strategy is vital to achieving all 2030 Agenda Goals. Economies with a diversified industrial sector and strong infrastructure better weathered the economic impacts of COVID-19 and recovered faster than other economies. The effects of the pandemic were highly heterogeneous across sectors, firms and workers, both globally and in Asia and the Pacific. The most vulnerable were small and medium enterprises, sectors highly integrated into global or regional value chains, and firms and workers in the informal economy. Women, youth and low-skilled workers suffered the most severe losses. Although economic activity in the region has quickly recovered, reaching its highest growth rate since 2010 in 2021, recovery remains incomplete and uneven, impacting progress towards SDG 9. Except for significant advances in two targets (9.a and 9.c), other areas are lagging. Countries in Asia and the Pacific must implement actions to accelerate progress on these targets, particularly those relating to manufacturing employment, transition to higher-technology industrial sectors and research and development (R&D) activity (9.2, 9.5, 9.b). Better and more disaggregated data are also needed to guide and monitor policies to improve infrastructure, promote innovation and enhance green and inclusive industrialization.

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SDG 9: Industry, Innovation & Infrastructure

Sustainable infrastructure and industrialization are essential for achieving the United Nations Sustainable Development Goals (SDGs). Infrastructure provides the foundation for economic growth and development, while industrialization creates jobs and opportunities. However, traditional infrastructure and industrialization models are often unsustainable. SDG 9 aims to build resilient infrastructure, promote sustainable industrialization, and foster innovation.

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The Effect of Public–Private Partnerships on Innovation in Infrastructure Delivery

Introduction, innovation in the infrastructure sector, public–private partnerships, effects on the innovation of complex relations among different elements in ppps, declaration of conflicting interests, appendix. detailed information of interviewees, biographies, cite article, share options, information, rights and permissions, metrics and citations, figures and tables, project finance, the economics of ppps, spv organizational design, spvs as integrators and quasi-permanent corporations, partnership and collaboration, the influence of performance-based specifications.

PPP Bid Process Encourages Innovation

Financial constraints on radical innovation, contract design limits innovation, contract design reduces ppp incentives to innovate, finance limits spv agency for autonomy, finance limits spv integration and partnership, performance specifications and payment schemes limit innovation, paradoxical effects of long-term contracts on innovation.

Project Finance’s Direct Effects on Innovation

Project finance and contract relations’ indirect innovation effects, project finance indirectly affects innovation by framing power relations, reducing project uncertainty paradoxically limits innovation.

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Attaining the 2030 Sustainable Development Goal of Industry, Innovation and Infrastructure

ISBN : 978-1-80382-576-2 , eISBN : 978-1-80382-573-1

Publication date: 11 July 2022

Hales, R. and Birdthistle, N. (2022), "The Sustainable Development Goals – SDG#9 Industry, Innovation and Infrastructure", Birdthistle, N. and Hales, R. (Ed.) Attaining the 2030 Sustainable Development Goal of Industry, Innovation and Infrastructure ( Family Businesses on a Mission ), Emerald Publishing Limited, Leeds, pp. 1-8. https://doi.org/10.1108/978-1-80382-573-120221001

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Related Papers (5)

Innovation practice and its performance implications in small and medium enterprises (smes) in the manufacturing sector: a resource‐based view, sme innovation in the malaysian manufacturing sector, the relationship between distinctive capabilities, innovativeness strategy types and the export performance of small and medium-size enterprises (smes) of malaysian manufacturing sector, determinants of internationalization: differences between service and manufacturing smes, the potential of innovativeness and eco-innovativeness of small and medium manufacturing companies in poland, trending questions (3).

- Subgoals: Industry, innovation, infrastructure development. - Indicators: Manufacturing growth, technological innovation, resilient infrastructure.

- Diversified industrial sector and strong infrastructure lead to faster recovery. - Higher technology industries recover faster, showcasing importance of innovation.

The provided paper does not mention UNESCO's SDG no. 7 or its aim to promote sustainable infrastructure and industrialization.

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GOAL 9: Industry, innovation and infrastructure

Learn more about SDG 9

Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation:

Constructing new greener infrastructures, retrofitting or reconfiguring existing infrastructure systems and exploiting the potential of smart technologies can greatly contribute to the reduction of environmental impacts and disaster risks as well as the construction of resilience and the increase of efficiency in the use of natural resources.

Data and Statistics / Facts and Figures:

In countries where data are available, the number of people employed in renewable energy sectors is presently around 2.3 million. Given the present gaps in information, this is no doubt a very conservative figure. Because of strong rising interest in energy alternatives, the possible total employment for renewables by 2030 is 20 million jobs.

Targets linked to the environment:

• Target 9.1: Develop quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all
• Target 9.2: Promote inclusive and sustainable industrialization and, by 2030, significantly raise industry’s share of employment and gross domestic product, in line with national circumstances, and double its share in least developed countries
• Target 9.4: By 2030, upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies and industrial processes, with all countries taking action in accordance with their respective capabilities
• Target 9.a: Facilitate sustainable and resilient infrastructure development in developing countries through enhanced financial, technological and technical support to African countries, least developed countries, landlocked developing countries and small island developing States

Related Sustainable Development Goals

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• Published: 29 February 2024

Does large-scale research infrastructure affect regional knowledge innovation, and how? A case study of the National Supercomputing Center in China

• Haodong Yang 1 ,
• Li Liu 2 &
• Gaofeng Wang 1  

Humanities and Social Sciences Communications volume  11 , Article number:  338 ( 2024 ) Cite this article

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• Science, technology and society

Large-scale research infrastructures (LSRIs) are widely acknowledged as a crucial instrument for venturing into the uncharted territories of science and technology, as well as contributing to the well-being of society. However, only a limited number of literature have scrutinized the impact of LSRIs, founded upon a causal inference framework. Moreover, the function of LSRIs in the advancement of innovation at the regional level remains inadequately identified. Drawing on the resource-based view, this study develops a conceptual framework that links the scientific effect of LSRIs to innovation resources in order to assess their impact on knowledge innovation (KI). Taking China’s National Supercomputing Center (NSC) as a case, three major mechanism hypotheses are proposed for the impact of NSC on KI, including basic effect, network effect, and technology effect. Using panel data from 283 cities in China from 2000 to 2020, we employ a spatial difference-in-differences estimation model to examine the impact of NSC on KI. The research finds that: (1) The construction of NSC stimulates KI in local and surrounding areas. (2) The main mechanisms by which NSC promotes KI include the increase in fiscal investment and talents in science, the improvement of digital infrastructure, as well as the enhancement of urban network centrality and innovation efficiency. (3) Geographical proximity, cooperation proximity, and digitization proximity constitute the main channels of policy spillover. (4) NSC has not shown significant promotion of regional innovation convergence, and its radiation influence needs further improvement. (5) The knowledge innovation effects of NSCs manifest heterogeneity based on the distinct knowledge orientation and innovation environment, with this impact being notably pronounced in application innovation-oriented cities such as Shenzhen. The results of this study reveal the positive yet limited impact of NSC on KI and provide a reference for other economies in the areas of LSRIs, digital infrastructure, and the formulation of place-based innovation policy.

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Introduction.

In the era of the knowledge economy, the value of scientific knowledge far surpasses any previous era. As the environment, health, energy, and population issues become increasingly complex, the information entropy of research objects is showing exponential growth. Against this backdrop, Large-scale research infrastructures (LSRIs) serve as important instruments for exploring the forefront of science and technology (S&T) and providing social public value. Their role in social and economic development is becoming more prominent (Michalowski, 2014 ; Beck and Charitos, 2021 ). LSRIs are regarded as large scientific platforms or systems consisting of clusters of large scientific instruments, facilities, and equipment (Michalowski, 2014 ; Qiao et al., 2016 ; D’ippolito and Rüling, 2019 ). The construction and operation level of LSRIs represents the strength of a country or region’s core original innovation ability (Marcelli, 2014 ). Therefore, LSRIs are particularly important for emerging countries that hope to catch up with developed countries in the field of S&T. Despite their significant demonstration and radiation effects, LSRIs have long been a topic of controversy due to their high technological complexity, long development cycles, and huge investment (Jiang et al., 2018 ; D’ippolito and Rüling, 2019 ). During the construction and operation stages, LSRIs usually face new and variable challenges involving multiple disciplines. The high complexity and uncertainty make failure easy and cause huge economic losses (Beck and Charitos, 2021 ). Therefore, it is particularly important to scientifically evaluate the knowledge effect produced by LSRIs. Existing research has explored the definition (Michalowski, 2014 ; Qiao et al., 2016 ; D’ippolito and Rüling, 2019 ), type (Qiao et al., 2016 ), and distribution (Marcelli, 2014 ) of LSRIs, analyzed the scientific effect that LSRIs possess in theory (Michalowski, 2014 ; Qiao et al., 2016 ) and investigated specific infrastructure using scientometrics or case study methods (Lozano et al., 2014 ; Carrazza et al., 2016 ; Caliari et al., 2020 ). However, as some studies have pointed out, there are few systematic evaluating the effect of LSRIs based on a causal inference framework (Bollen et al., 2011 ), and further efforts are needed to identify the role of LSRIs in innovation growth at the regional level (Caliari et al., 2020 ; Beck and Charitos, 2021 ).

In modern scientific research and technology engineering, complex mathematical calculations beyond human cognitive abilities are frequently encountered and must be solved using computers (Bollen et al., 2011 ; LeDuc et al., 2014 ). High-performance computing applications have integrated modeling, algorithms, software development, and computational simulation, serving as a necessary link for the application of high-performance computers in cutting-edge basic scientific research and becoming a third scientific method apart from theoretical research and scientific experiments. This study focuses on the impact and mechanisms of China’s National Supercomputing Center (NSC) on knowledge innovation (KI). The reasons for choosing NSC in China as the research object are: on the one hand, China has attached great importance to large-scale scientific facilities and their associated research in recent years, particularly in the field of supercomputing. The national and local governments have cumulatively invested billions of dollars and constructed more than ten national-level supercomputing centers. Among them, the Tianhe-1, Tianhe-2, and Sunway TaihuLight supercomputers are representative examples that have consistently ranked among the top ten on the TOP500 list, making China one of the few developing countries to achieve this level in large-scale scientific infrastructure investment. Investigating whether LSRIs investment enhances computational capabilities and contributes to scientific productivity or is merely an ambitious “image project” (public image campaign) is of great significance. On the other hand, since 2009, China has established NSCs in some cities (not traditional first-tier cities such as Beijing and Shanghai), which can be viewed as an external shock for local development. This provides a prerequisite for evaluating the scientific effect of NSCs under a causal inference framework, especially given that the goals and application fields of NSCs are rooted in local innovation endowment and industrial foundation (as a place-based policy). Taking Tianjin (one of the four municipalities directly under the central government of China) as an example, the construction of NSC has promoted the establishment of Tianhe S&T Park and the Industrial Big Data Application Innovation Center, aiming to build an industrial innovation system that integrates industry, academia, and research, promoting local talent cultivation and international cooperation.

Considering the unique attributes of NSC, this study’s research topic and objectives not only aim to address the ongoing debate regarding the role of LSRIs but also encompass the following two aspects:

As an extension of digital infrastructure.

Influenced by Schumpeterian innovation theory, introducing new technology and utilizing the power of “creative destruction” to enhance production levels are regarded as crucial factors for regional economic growth (Cardona et al., 2013 ; Batabyal and Nijkamp, 2016 ). The emergence and widespread use of information and communication technology (ICT) have fostered the digital economy. With the development of 5G communication, big data, and artificial intelligence, digital technology is increasingly viewed as a radical new technology. Existing literature confirms the broader effects of digital infrastructure construction on economic growth, urban innovation, corporate transformation, and social development (Cardona et al., 2013 ; Balcerzak and Bernard, 2017 ; Zhou et al., 2021 ; Zhang et al., 2022 ; Tang and Zhao, 2023 ). The majority of this literature are focused on scrutinizing the effects of network infrastructure, while relatively less attention has been given to the role of computing infrastructure, specifically its impact on promoting scientific knowledge production. This study seeks to provide evidence of the NSC’s influence on regional knowledge innovation as computing infrastructure.

As a practice of place-based innovation policy.

The scale effect of agglomeration leads to an increase in research and development (R&D) factor demand and releases the self-reinforcing characteristics of innovation, which may hinder the catching up of backward areas with advanced areas and have a negative impact on overall regional competitiveness and inclusiveness. Place-based innovation policy plays a crucial role in promoting coordinated regional innovation with the aim of achieving innovation convergence (Barca et al., 2012 ; Liu and Li, 2021 ). Although this policy model follows the principle of differentiation, some scholars are cautious about intervention, believing that government intervention may distort resource allocation, resulting in a loss of innovation efficiency, or point out that the impact of local policies is limited (Neumark and Simpson, 2015 ; Lu et al., 2022 ). As Marcelli ( 2014 ) mentioned in his study, scientific infrastructure is often situated in specific geographic locations, as evidenced by the establishment of multiple NSCs in different cities. Therefore, this study can also be seen as an evaluation of place-based innovation policy, involving identifying how supercomputing centers contribute to the development of local and regional knowledge innovation.

Compared with existing literature, this study aims to make several theoretical contributions:

First, drawing on the resource-based view, we categorize urban innovation resources into tangible resources such as human, financial, and physical capital, and intangible resources including social capital and resource utilization efficiency. This process not only shifts the focus from enterprise strategic resources to regional innovation resources but also integrates the resource-based view with social network theory and innovation efficiency research. Second, we establish a link between the scientific effect of LSRIs and the resource-based view, mapping the four scientific effect dimensions of S&T advancement effect, capability cultivation effect, networking effect, and clustering effect to innovation resources (both tangible and intangible). By extending the evaluation of LSRIs to the regional level, we provide empirical evidence for the causal relationship between LSRI and their innovation performance. Third, we classify the mechanism of NSC’s impact on regional knowledge innovation into three representative effects: the basic effect represented by R&D expenditure, S&T human resources, and digital infrastructure; the network effect represented by urban innovation network centrality; and the technological effect represented by innovation efficiency. By utilizing the convergence model, we verify the policy spillover of LSRIs and elucidate the role of computing infrastructure construction as a place-based innovation policy in regional innovation.

This article first discusses the definition and scientific effect of LSRIs, and based on the resource-based view, constructs a conceptual framework of LSRIs’ impact on knowledge innovation through mapping the scientific effect to different innovation resources. Next, we propose three mechanisms of NSC that affect KI (basic effect, network effect, and technology effect) and briefly review the development history of NSC in China. Then, the data, methods, and estimation results are presented. Finally, we discuss, and summarize the research results, and suggest policy implications.

Theoretical basis and evaluation framework

Scientific effect of large research infrastructures.

LSRIs, which are scientific research facilities built to meet the needs of modern “big science” research, aim to expand human cognitive abilities, discover new laws, and incubate new technologies. Some studies have divided the roles of LSRIs into categories including, but not limited to, scientific, technological, economic, educational, and other social aspects (Marcelli, 2014 ; Michalowski, 2014 ; Qiao et al., 2016 ; Carrazza et al., 2016 ; Caliari et al., 2020 ; Beck and Charitos, 2021 ). The OECD report in 2014 partitioned the impacts of LSRIs into scientific achievements, impacts of construction and operation, personnel training, scientific cooperation, technological innovation, and education (Michalowski, 2014 ). Qiao et al. ( 2016 ) established an analytical framework to evaluate the implementation effects of LSRIs, deconstructing the scientific effect of LSRIs from the perspectives of the S&T advancement effect, capability cultivation effect, networking effect, and clustering effect. Caliari et al. ( 2020 ) considered that LSRIs can make significant contributions to the economic growth of developing countries through technology and innovation, with specific roles involving scientific output and technological progress, supporting the development of industrial, health, and agricultural sectors. some scholars have explored the specific impacts of LSRIs in a targeted manner, such as D’ippolito and Rüling ( 2019 ) who discussed the types and formation of cooperation and their impact under the background of LSRIs sharing. Scarrà and Piccaluga ( 2022 ), aiming to understand how big science affects innovation through transfer mechanism and spillover effect, reviewed the relevant research directions through literature surveys, covering six major themes including technology transfer methods and mechanisms, cooperation with the public sector, and spillover effects of LSRIs, etc.

This article aims to examine the impact of NSC, a type of LSRI, on regional knowledge innovation and its mechanism. Establishing a framework is a prerequisite for conducting the evaluation. Given that the area of the research sample is China, in order to better fit the institutional and developmental background, we build the framework based on Michalowski ( 2014 ) and Qiao et al. ( 2016 ) and integrate the resource-based view to construct the path of NSC’s impact on knowledge innovation.

Resource-based view, social network theory, and innovation efficiency

The resource-based view emphasizes that an organization’s success is rooted in its specific resources, which constitute the logical starting point for strategic decision-making. The impact of resources on an organization’s competitive advantage applies not only to the enterprise level but also to the competitiveness of regions and countries, which depend on their resource endowments (Porter, 1990 ; Fatima et al., 2022 ; Ge and Liu, 2022 ). The study of the firm has identified specific forms of resources, with Grant ( 1991 ) proposing six major resources, including financial resources, physical resources, human resources, technological resources, reputation resources, and organizational resources. Das and Teng ( 1998 ) divided resources into financial, managerial, material, and technological categories. These heterogeneous resources can be classified into different groups based on different criteria, such as tangibility or whether they are protected by property law. In the context of innovation, Del Canto and Gonzalez ( 1999 ) categorized R&D resources into three types: financial, physical (capital intensity), and human resources. Auranen and Nieminen ( 2010 ) argued that organizations ensure the continuous development of R&D activities by acquiring and possessing equipment, funding, and personnel. Of course, the development of urban knowledge innovation not only depends on the direct input of local R&D resources but also the interaction with other regions. Social network theory asserts that the manner in which events unfold is contingent upon the context in which they take place. From the perspective of social capital, networks have significant value in transmitting resources (Beck and Charitos, 2021 ; Wei et al., 2022 ). Thus, relationships established through interaction and the networks formed through accumulation become important channels for information and knowledge diffusion. Similar views also appear in studies of the knowledge-based view (KBV), with the practicality of knowledge determining the need for interaction with external groups, and regions can effectively supplement their local knowledge resources through external knowledge spillover channels (Das and Teng, 1998 ; Ge and Liu, 2022 ). Overall, the RBV regards the creation and maintenance of networks as a mechanism for acquiring scarce resources, and the degree of embedding of regions in knowledge innovation networks reflects their implicit social capital resources.

However, in many instances, an organization’s success is not determined by its possession of superior resources, but rather by its ability to effectively utilize them. Simply possessing specific resources does not guarantee an organization’s competitive advantage, thus rendering resource utilization a critical issue in resource-based theory research (Majumdar, 1998 ; Arbelo et al., 2021 ). The high uncertainty of R&D activities and the limited quantity of R&D resources make it insufficient to merely explore resource input. This issue is relevant to the use of resources at the micro level, like in businesses, universities, and laboratories, as well as at the macro level, including in cities, regions, and even countries. It involves how to optimize resource input efficiency, such as using fewer resources to support the same level of business (output) or using existing resources to support more business (output). Relevant literature in the field of innovation suggests that knowledge production efficiency or innovation efficiency can be understood as the level of innovation potential formed by different R&D resources, i.e., the degree to which innovation input is converted into actual innovation output (Bai et al., 2020 ). The efficiency level is often related to the institutional background, organizational model, and internal structure of the research subject (Li, 2009 ). For a city, the innovation efficiency of a region is influenced by the internal innovation organization and element structure, as well as the innovation environment.

Based on the above discussion, we consider that the RBV provides a conceptual framework for examining the impact of NSC on knowledge innovation. This view is aligned with numerous dimensions of scientific effect in LSRIs. At the regional level, innovation factors, including R&D funding, and human and material capital, are regarded as the basic components of inter-regional innovation capacity, or as the core inputs for knowledge production. In this study, we take these factors as innovation resources unique to the local area and possessing tangible characteristics. This can be mapped to the capability cultivation effect (talent cultivation) and clustering effect (innovation agglomeration) in scientific effect. Furthermore, considering that the cross-regional networks formed by the interaction of cities with other regions constitute one of the main channels for information exchange and knowledge spillover, we view the embedding of cities in the innovation network as one of the main ways to obtain external resources (networking effect in LSRIs scientific effect), or as understanding the social capital resources of cities possessing intangible characteristics. Finally, given that the utilization of resources (affected by technological progress, institutional factors, and agglomeration, mapping to the capability cultivation effect and clustering effect) in increasing regional competitive advantages is as important as resource acquisition, we consider it as another intangible resource besides social capital at the regional level. In this way, we have achieved an integration of the resource-based view, social network theory, and innovation efficiency, forming a conceptual framework for NSC to influence knowledge innovation by changing regional innovation resources, which is linked to existing dimensions of scientific effect (Fig. 1 ).

The mapping of resource possession and utilization, along with the various dimensions of scientific effects within LSRIs, has achieved the integration of the resource-based view, social network theory, and innovation efficiency. The conceptual framework, wherein NSC influences KI, is thereby constructed.

Research hypothesis

Basic effect.

Similar to other infrastructures, the construction of NSC also has a knowledge spillover effect (across technological fields). NSC not only provides high-performance computing services but also has a complete application software environment. With accumulated research achievements and industry big data, the center can achieve the integration of supercomputing, big data, and artificial intelligence through R&D, construct a supercomputing application network, provide resources and platforms for digital services, and foster emerging industries in the supercomputing field. The NSC in Tianjin includes the supercomputing center, cloud computing center, e-government center, big data, and artificial intelligence R&D environment, aiming to promote the rapid development of the digital industry in the local and surrounding areas. It should be emphasized that the digital infrastructure requires financial support from the government, which often provides funding for R&D activities conducted by both public and private institutions (Gao and Yuan, 2020 ). The social benefits of R&D activities cannot be fully internalized by market mechanisms, making the government’s fiscal intervention somewhat reasonable. The construction of LSRIs represented by NSC requires a large investment and involves high risks, and there is a lack of sufficient motivation for private capital involvement. Given that the establishment of NSC relies heavily on fiscal investment as a key driver, the increase in government fiscal expenditures is needed to provide support for R&D and operation. Furthermore, the government may raise its S&T expenditures by increasing the number of project applications, which could provide financial support for universities, research institutions, and enterprises to purchase computing power, thereby facilitating efficient scientific research. Finally, talent is the key to influencing a city’s innovation and learning abilities. On the one hand, the construction and operation of the NSC require professionals in high-performance computing, computer networks, parallel software, and distributed systems, who should possess relevant industry experience and professional knowledge. On the other hand, investment in the new digital technologies and knowledge spillover from the NSC will drive the development of digital and other emerging industries. These high value-added and knowledge-intensive industries will in turn attract more S&T talents and enhance the regional innovation competitiveness. Based on the above, this study hypothesizes:

H1. NSC can promote the development of knowledge innovation by influencing regional financial resources (fiscal S&T expenditure), human resources (S&T talents), and material resources (digital infrastructure). We also define this as the basic effect of NSC on KI.

Network effect

Digital infrastructure characterized by informatization and networking can overcome the barriers of temporal and spatial distance in scientific research activities. It not only connects innovative fields that were previously isolated, promoting knowledge convergence and recombination, but also facilitates long-distance knowledge dissemination that would otherwise be constrained by geographic limitations (Qiao et al., 2016 ; da Silva Neto and Chiarini, 2023 ). To acquire high-end digital technology services, other cities are often more willing to establish cooperative relationships with NSC cities. Such cooperation can not only enhance the strength of existing collaborative interactions but also potentially form new cooperative relationships. The embedding of the innovation network structure can increase the centrality of the city in the network, which contributes to the attainment of more information and resource benefits (Han et al., 2021 ; Wen et al., 2021 ). Cross-regional cooperation connects innovation organizations with heterogeneous knowledge, which can alleviate the innovation reduction caused by homogeneous knowledge at the local level (Hazır et al., 2018 ). Existing research has revealed the negative impacts of excessive centrality. The establishment and maintenance of social relationships incur a certain cost, while excessive embedding of network structures may lead to increased maintenance costs, potentially crowding out innovation resources and generating diseconomies of scale (Wang et al., 2014 ). Complex connections imply exposure to more information, which poses challenges to information screening, and integration, and even leads to information overload. NSC relies on the supercomputer consisting of thousands of processors and extends the development of new digital technologies such as big data, artificial intelligence, and cloud computing, enabling the storage and recognition of massive amounts of information and knowledge. This reduces the cost of network maintenance, and cities can improve efficiency in the processes of capturing external information, absorbing knowledge, and maintaining external relationships, thereby enhancing the positive impact of urban research network embedding and encouraging cities to be involved in scientific research cooperation networks actively. Empirical evidence shows that the NSC in Chengdu has offered computing services to over 760 users across 35 cities, including major metropolitan areas such as Beijing, Shanghai, Guangzhou, and Chongqing. The NSC in Tianjin provides computing services to over 30 provinces, municipalities, and autonomous regions across the country, with more than ten partner institutions, including universities like Peking University, Dalian University of Technology, Jilin University, and Harbin Engineering University, as well as local government such as Linyi City. Additionally, it has established joint laboratories with 17 institutions, to support basic research and technological innovation. Based on the above analysis, it is reasonable to propose the hypothesis:

H2. The construction and operation of NSC can promote the development of knowledge innovation by affecting the embedding of the region in the national scientific research network (i.e., social capital resources), which is also defined in this study as the network effect of NSC’s impact on KI.

Technology effect

The impact of NSC on the utilization capacity of regional S&T resources is mainly reflected in two aspects: (1) computing efficiency. The emergence of new data features has brought about governance challenges such as how to handle, store, transmit, and analyze data, while also driving a paradigm shift in scientific research. In fields such as drug testing, genomics research, climate simulation, energy exploration, molecular modeling, and astrophysics, high-dimensional and massive data impose higher demands on computing power and memory. The main features of supercomputers include two aspects: fast data processing speed and large data storage capacity. For the former, the computing speed of supercomputers can currently reach more than hundreds of billions of times per second in China, which is millions of times faster than ordinary computers. The peak computing speed of the “Tianhe-2” system is 100.7 PFlops, and the sustained computing speed is 61.4 PFlops; for the Sunway TaihuLight supercomputer, these two data are 125.4 Pflops and 93.1 Pflops, respectively. As for the latter, the total storage capacity has also reached dozens of PetaBytes. The supercomputing center is equipped with various peripheral devices and high-functional software systems, which will greatly shorten the cycle of innovation, and reduce the cost and uncertainty of innovation. (2) Allocation efficiency. The limited ability of the public sector to acquire information and knowledge, along with the potential for policy lag, may result in interventions that fail to produce the expected effects. In the context of NSC construction, supporting the entire process of data collection, sharing, computation, analysis, and application with computing power can significantly improve the level of intelligence, precision, and scientific decision-making in social governance. Governments can use digital technology tools to better plan and formulate S&T policies. Through multi-disciplinary and cross-departmental information sharing, supercomputing centers can leverage new technologies such as cloud computing, big data, and the Internet of Things to optimize the integration and allocation of scientific and technological resources and achieve scientific decision-making for S&T expenditures. By helping to build digital platform for urban S&T resources, the correlation mapping of different data such as projects, expenditures, and results can be achieved, providing support for project progress, result evaluation, and further funding decisions. NSC and its supporting digital infrastructure construction can significantly improve the efficiency of data collection, processing, transmission, storage, and other aspects on both the supply and demand sides. The cloud platform is configured with multiple databases such as scientific research results and talents, achieving “dual-linkage” between scientific research results and innovation needs. This model mitigates the temporal and spatial limitations of the interaction between supply and demand for R&D elements and reduces the cost of search, effectively improving the city’s ability to integrate S&T resources. Based on the above analysis, hypotheses are put forward:

H3. The construction and operation of NSC can promote the development of knowledge innovation through the improvement of computing and allocation efficiency. We also define this as the technology effect of NSC affecting KI.

Innovation convergence and knowledge diffusion

The importance of knowledge in modern economic development is increasing, and regional innovation is becoming increasingly reliant on close spatial associations. In other words, the growth of innovation in a region depends not only on the local accumulation of knowledge but also on the diffusion of knowledge from neighboring areas, which is one of the potential opportunities for promoting regional innovation development (Tang and Cui, 2023 ). Existing literature indicate that the construction of digital infrastructure can help spread and diffuse knowledge (Batabyal and Nijkamp, 2016 ; Balcerzak and Bernard, 2017 ), while S&T centers can promote regional collaborative development through knowledge spillovers generated by innovation clusters (Gao and Yuan, 2020 ). Therefore, the construction of NSC, which combines digital infrastructure and scientific infrastructure, may also have an impact on the knowledge innovation development of neighboring areas. Firstly, this type of proximity relationship can be general geographic adjacency, as the flow of R&D factors and the effects of knowledge learning (especially tacit knowledge) are still influenced by geographical distance. The establishment of NSC in Kunshan aims to undertake advanced computing and scientific big data processing business in the Yangtze River Delta region, and engage in strategic cooperation with Suzhou Deep-time Digital Earth Research Center, Shanghai Neuroscience Research Center, and other institutions, to carry out applied computing research and services in scientific fields including artificial intelligence and biomedicine. Secondly, proximity can also refer to the economic distance, which in the context of this study can be understood as differences in the level of urban digitization. Research at the regional level applying the theory of innovation absorption indicates that the region requires relevant prior knowledge and a compatible cognitive structure to accurately identify, integrate, and effectively absorb valuable knowledge (Ge and Liu, 2022 ). If there is too large of a gap in digitalization levels between regions, latecomer regions lacking sufficient digital technology and a complete information communication environment are unlikely to benefit from the computational power services provided by NSC. Finally, the cross-regional cooperative network formed by interaction among knowledge production organizations is regarded as another channel for knowledge diffusion. Advanced regions can obtain exogenous knowledge that is different from local knowledge through cooperation. If knowledge, experience, and resources from advanced regions can diffuse to less advanced regions, innovators in the latter can use the absorbed knowledge to create new scientific outputs, bridging the knowledge gap with the scientific forefront (De Noni et al., 2018 ; Erdil et al., 2022 ). The establishment of NSC could create policy spillover through the provision of computational power services to previously closely connected partners, thereby promoting local knowledge innovation and development. Based on the above analysis, we propose the following hypotheses:

H4a. The establishment of the NSC may facilitate policy spillover through proximity relations.

H4b. Building on existing policy spillover, the NSC fosters regional knowledge innovation convergence.

Development process and institutional background of NSCs

Main supercomputers in china.

(1) The Tianhe series: In 1978, the Chinese government put forward the aim of creating a supercomputer and assigned the National University of Defense Technology with the task. The first computer in China that performs calculations at a speed of more than 100 million times per second, named “Yinhe-I”, was successfully appraised in Changsha in 1983, and subsequent models were released in succession. With the accumulation of previous experience and technology, the “Tianhe-1” and “Tianhe-2” were developed by the university and achieved top ranking in the TOP500 list in 2010, 2013, and 2015 respectively (please refer to Table 1 for details, including serial number 1, 3, and 4). (2) Dawning series: The “Dawning” series of supercomputers was developed by the Institute of Computer Science at the Chinese Academy of Sciences. In 1993, the “Dawning-I” was successfully developed, making a breakthrough for China in the field of Symmetric Multi-Processing. In 2010, the “Dawning Nebula” supercomputer achieved second place on the world’s supercomputer list (TOP500), representing the most significant accomplishment of the “Dawning” series (refer to number 2 in Table 1 for details). (3) Sunway series. In 1996, the National Research Center for Parallel Computer Engineering and Technology was established, signaling the beginning of the development of the “Sunway” supercomputers. In 2010, the “Sunway Blue Light” was created, and subsequently situated at the NSC in Jinan. In 2016, the “Sunway TaihuLight” supercomputer achieved first place on the TOP500 list (refer to number 6 in Table 1 for details). For a comprehensive exposition on the intricate trajectory of high-performance computing development in China, kindly refer to the work by Wang ( 2023 ).

Supercomputing center in China

The NSCs were established by the Ministry of Science and Technology with the aim of providing high-performance computational resources for scientific research in China. Since 2009, NSCs have been approved and constructed in different cities, including Tianjin, Shenzhen, Changsha, Guangzhou, Jinan, Wuxi, and Zhengzhou. The central government cooperates with local governments to jointly fund the construction of NSC. During the operation phase, some operating expenses are subsidized by local government finances. At the same time, central and local governments open topic applications to some researchers, who use part of the research funds to purchase computing resources. Currently, the NSCs, along with their supporting data storage and backup centers, have been applied in various fields such as biomedicine, genetics, aerospace, climate, marine science, artificial intelligence, new materials, new energy, neuroscience, and smart cities. Taking the NSC in Tianjin as an example, the “Tianhe-1” supercomputer has supported more than 2000 national science and technology major projects and national key R&D programs during its operation, with total funding exceeding 2 billion yuan. It has been recognized with both national and provincial-level awards, contributing to thousands of published academic achievements. (refer to https://www.nscc-tj.cn/index ).

Data and methodology

Data and variables, dependent variables.

This paper aims to explore the impact of NSC on regional knowledge innovation. Broadly speaking, KI involves multiple aspects including knowledge acquisition, creation, and transformation, among which knowledge creation is the core. Innovation organizations such as universities and research Institutes engage in basic and applied research to pursue new scientific discoveries and generate new scientific knowledge. Scientific publications serve as the primary carrier of scientific knowledge, reflecting the latest advances in scientific research, and also an important channel for knowledge diffusion across different research fields and geographic locations (Qiao et al., 2016 ). The output of scientific papers in a city to some extent reflects the level of knowledge innovation in the region (Li, 2009 ; Yang et al., 2022a ). Meanwhile, the publications database provides public access to the city information to which the researchers belong. Therefore, this study characterizes the level of knowledge innovation ( KI ) by the per capita number of scientific publications in a city and specifically measures it by the number of papers included in the Science Citation Index (SCI). In the robustness test, the criterion for highly cited SCI papers is defined as follows: sorting the number of citations of SCI papers in the same discipline and the same year, the papers ranked in the top 1% are considered as highly cited papers.

Independent variable

The variable NSC is a dummy variable that takes a value of 1 if the city approves the construction of an NSC in the current year or any year thereafter, and 0 otherwise.

Mechanism variables

(1)The basic effect variables. Firstly, considering the significant role of financial investment in the construction and operation stages of LSRIs, government financial support for S&T ( R&D_exp, billion yuan ) is selected as the proxy variable for R&D expenditure (Li, 2009 ; Liu and Li, 2021 ; Ge and Liu, 2022 ). Secondly, the scientific and technological talent (Li, 2009 ; Gao and Yuan, 2020 ) (human resources) is represented by the number of employees who conduct scientific research and technology services in the city ( R&D_talent , 10,000 persons ), which is essential for the construction, operation, and radiation effects of the NSC, requiring a sufficient number of knowledge-based personnel, particularly in STEM fields. Thirdly, digital infrastructure construction (physical resources) covers areas such as 5G, artificial intelligence, and industrial internet, reflecting the development of technological and material resources in the context of NSC (Zhang et al., 2022 ; Tang and Zhao, 2023 ). The construction of digital infrastructure is indirectly characterized by the city’s digitalization index, constructed using the entropy method based on secondary indicators including the number of mobile phone users and internet users, revenue of postal and telecommunications industry, number of relevant employees (in information transmission, computer services, and software industry). (2) The network effect variables. The network effect variable is related to the centrality and structural hole of actors in the network. Generally, an actor’s position in a network is considered more important if they have higher centrality and more structural holes. This study focuses on whether the construction of NSC can affect the direct connection between NSC cities and other regions, as well as its embedding in the network structure, rather than focusing on the control of knowledge flow between nodes. Therefore, we select indicators that reflect urban centrality, including degree centrality and closeness centrality (Wang et al., 2014 ; Han et al., 2021 ). (3) The technology effect variable. To measure the technology effect of NSC in terms of innovation efficiency ( Innova_effi ), we employ the stochastic frontier analysis (SFA) method, which is rooted in economic theory and allows for a more rigorous measurement of innovation efficiency (Li, 2009 ).

Control variables

(1) Economic development (Liu and Li, 2021 ; Lu et al., 2022 ; Gao and Yuan, 2020 ). Characterized by per capita GDP ( Econ, 10,000 yuan ). (2) Industrial structure (Tang and Cui, 2023 ; Lu et al., 2022 ): Measured by the share of the secondary industry in the GDP ( Industry_sec , % ). (3) Comprehensive growth rate ( n  +  g  +  δ ): Modeled according to the research of Yang (2021), and calculated as the sum of natural growth rate, technological progress rate, and capital depreciation rate, assuming that the sum of technological progress rate and capital depreciation rate equals 5%. (4) Financial sector development: Gauged by the aggregate amount of loans and deposits held by financial institutions ( Finan , 10,000 yuan). (5) Human capital (Tang and Cui, 2023 ; Liu and Li, 2021 ; Gao and Yuan, 2020 ): Proxied by the number of university students per 10,000 individuals ( H_cap ). (6) Traffic and Openness (Yang et al., 2021 ; Gao and Yuan, 2020 ): Indicated by the total volume of passenger transport via road, water, and air transport for openness and commuting ( Trans , 10,000 people).

Data sources and processing

The research sample in this study consists of panel data for 283 Chinese cities from 2000 to 2020. The number of papers indexed by the Science Citation Index (SCI) for each city is obtained from the Web of Science (WoS) database ( https://www.webofscience.com ). The centrality measure is based on constructing a city-level scientific research network matrix. To construct the matrix, information on scientific research collaborations among cities in China is obtained through Python. If authors from different cities appear in the same paper, it is considered as a collaboration between the cities, and the centrality of cities is then calculated using Ucinet software. The control and mechanism variables, including government financial support for S&T, the number of employees in scientific research and technology services, the number of mobile phone users and internet users, revenue of postal and telecommunications industry, number of relevant employees (in information transmission, computer services, and software industry), are obtained from the “China Urban Statistical Yearbook”.

Missing values are handled by the interpolation method or replaced with the mean of the city. As for the measurement of Innova_effi , we use government financial support for S&T, and the number of employees in scientific research and technology services as input indicators for financial and human resources. The output indicator is represented by knowledge innovation. Two models, the Cobb-Douglas production function model and the stochastic frontier analysis with translog function, are respectively used for calculation, and the generalized likelihood ratio test is used for verification. The results (LR chi2 = 77.76, P = 0.000) indicate that the stochastic frontier analysis with translog function is more suitable for calculating innovation efficiency. Descriptive statistics of the variables are presented in Table 2 , and natural logarithms are taken for some variables (including Econ, Finan, H_cap , Trans ) with values affected by price factors. As per Table 2 , it is discernible that the standard deviations of a series of variables, including KI, are notably higher than the means. This indicates significant data dispersion, unveiling substantial inter-city disparities, a fact also elucidated by the distinctions between the maximum and minimum values. To scrutinize the influence of skewed data on the estimation results, in the section “Robustness test”, we conduct a robustness examination by substituting models. Additionally, recognizing the disparities between the treatment and control groups (descriptive statistics of the groups are retained), we acknowledge potential disruptions from bidirectional causality and sample self-selection biases in the baseline regression results. To address this issue, the study employs methods including IV, 2SLS, PSM-DID, and placebo tests.

Methodology

Since 2009, the Ministry of Science and Technology has successively approved the establishment of NSCs in several cities. We take this as a quasi-natural experiment and regard cities with supercomputing centers as the treatment group and other cities as the control group to examine the effect of NSC on regional knowledge innovation. Due to differences in the construction time of supercomputing centers, the study first constructs a time-varying difference-in-differences (DID) model:

In the above equation, i and t represent specific cities and specific years, respectively. KI it is the knowledge innovation level of city i in t years. \({Z}_{{it}}^{{\prime} }\) represents other control variables that may impact the level of urban knowledge innovation. v i and μ t represent individual-fixed effects that do not vary over time and time-fixed effects that do not vary across individuals, respectively. Theoretical analysis indicates that the establishment of NSCs not only could promote local knowledge innovation but may also affect neighboring areas. Thus, this study uses the spatial DID method to relax the assumption that individuals are independent in classical DID. The most common spatial econometric models are the Spatial Lag Model (SAR), Spatial Error Model (SEM), and Spatial Durbin Model (SDM). Based on the spatial autocorrelation of the dependent variable (significant Moran’s I at 1% level for each year, as shown in Table 3 ), this study follows the selection criteria proposed by Elhorst ( 2014 ) and conducts LM and Wald tests on the sample. Moreover, The Hausman and LR tests are also been conducted to assess the appropriateness of using a two-way-fixed effects model. Ultimately, the benchmark regression model adopts the SDM, as presented below:

In formula (2), ρ represents the spatial autoregressive coefficient, β 2 and β 3 indicate the impact of NSC construction and control variables in spatially related areas on knowledge innovation in the focal area, respectively. W is the spatial weight matrix. The inverse distance matrix between cities is mainly used as the spatial weight matrix in this study. The matrix is calculated by using the longitude and latitude data of each city (obtained from Baidu Map API) to calculate the spherical distance between two cities.

To investigate the mechanism through which NSC affects knowledge innovation, this study employs Alesina and Zhuravskaya’s ( 2011 ) mechanism test method. Utilizing a linear model, we established the impact of NSC on mechanism variables. Subsequently, it conducts a comparative analysis of the estimated coefficients of NSC in equations that control for mechanism variables and those that do not, aiming to validate the existence of such mechanisms (Gao and Yuan, 2020 ; Zhang and Wang, 2022 ; Chen et al. 2023a ).

Assuming that the coefficient of NSC in Eq. ( 2 ) is significant, the mechanism variable is used as the dependent variable, and the treatment variable ( NSC ) is used as the independent variable for regression analysis. The specific formula is as follows:

If the coefficient of the NSC in Eq. ( 3 ) is significant, then NSC and each mechanism variable will be included in the regression model with knowledge innovation as the dependent variable, as shown in Eq. ( 4 ). If the estimated coefficient of NSC decreases or is not significant, it means that the construction of NSC can affect the development of urban knowledge innovation through the mediating variable path.

In addition to studying the diffusion of knowledge across geographic proximity, this study constructs two types of adjacency matrices: one based on digitization distance (constructed from the reciprocal of the absolute difference of each city’s digital infrastructure index) and the other based on collaborative frequency (constructed from the number of collaborations between each city and other cities). Furthermore, following the method of Sala-i-Martin ( 1996 ), this paper examines the impact of NSC on knowledge innovation convergence. A detailed description of the process is provided in the section “Methodology”. Based on the conceptual framework, mechanism analysis, and research design presented earlier, the final research framework of this our study is illustrated in Fig. 2 below:

NSC influences KI through basic effect, technology effect, and network effect, with the potential to shape regional knowledge innovation convergence through various proximities.

Benchmarking

Table 4 reports the estimated results of NSC’s impact on knowledge innovation under the geographic proximity matrix. Columns (1) and (2) present the OLS regression results controlling for time and city-fixed effects. It can be observed that the estimated coefficient of the treatment effect variable NSC is significantly positive at the 5% level or higher (10.191/8.958), regardless of whether control variables are included. Columns (3) and (4) show the results of the spatial econometric model, with both the LM test and Wald test statistics being significant at the 1% level, ensuring the validity of the SDM used. Specifically, the estimated coefficients of NSC are significantly positive at the 1% level (10.184/8.934), suggesting that the establishment of NSC promotes local knowledge innovation. The estimated coefficients of the interaction term NSC×w are also significantly positive at the 1% level (47.319/37.966), indicating that NSC construction has a positive impact on knowledge innovation in surrounding areas. This can be attributed to the continuous improvement of transportation infrastructure and digital networks (Yang et al. 2021 ; Zhang et al. 2022 ), as well as the regional development strategy represented by urban agglomerations (Tang and Cui, 2023 ). The estimated results in Table 4 provide empirical evidence about the impact of the supercomputing center on regional scientific knowledge production and suggest that neglecting policy spillover effects would underestimate the influence of NSC on urban knowledge innovation, which is unfavorable for policy evaluation.

Mechanism test

Test of basic effect.

To explore the mechanisms through which NSC affects knowledge innovation, we conduct empirical tests from three levels: basics effect, network effect, and technology effect, based on the theoretical analysis in the section “Research hypothesis”.

Table 4 focuses on the basic effect of NSC, and column (4) in Table 4 indicates that NSC construction significantly improves urban knowledge innovation performance. The second-stage regression results, shown in columns (5), (7), and (9) of Table 5 , indicate that the policy treatment effects are all significantly positive at the 1% level (6.216/3.228/0.057). This result suggests that NSC construction not only promotes regional S&T investment and an increase in R&D personnel but also helps improve digital infrastructure. Furthermore, similar to the knowledge innovation performance, the construction of NSC can also have a positive effect on the financial, human, and material resources of innovation in geographically adjacent regions. The phenomenon can be interpreted through existing literature. For instance, the Chinese government’s integration of S&T investment targets in the evaluation criteria for local officials has stimulated innovation competition, compelling neighboring city governments to augment their S&T investment (Liu et al., 2020 ; Gao and Yuan, 2020 ). Alternatively, the regional integration development strategies have minimized inter-regional transit time, facilitating the flow of R&D elements (Tang and Cui, 2023 ; Yang et al., 2021 ). The increase in S&T investment, as well as the aggregation of talent, will also propel industrial structure upgrading (Gao and Yuan, 2020 ), expedite the construction of digital infrastructure, and enable urban digital transformation.

Finally, columns (6), (8), and (10) in Table 5 show the third-step regression results, in which the S&T investment, R&D personnel, digital infrastructure, and treatment effect variables are all simultaneously included in the regression equation. The estimated coefficients of the mechanism variables are all significantly positive at the 1% level (1.023/1.369/90.641), and the promotion effects of NSC on knowledge innovation are still significant, but the absolute values of the coefficients have decreased. Theoretically, the innovation effects of investments in S&T, R&D personnel, and digital infrastructure have substantial empirical support. Firstly, concerning the impact of investments in S&T on innovation, a substantial body of research has demonstrated the stimulating impact of government subsidies on the R&D activities of enterprises. Studies have also focused on the innovation effects of public sector S&T investments, encompassing different dimensions like research institutes and cities, supporting that fiscal investment in S&T has led to an increase in both the quantity of scientific publications and patents (Link and Scott, 2021 ; Chen et al., 2023b ). Secondly, since the proposition of endogenous growth theory, the accumulation of human capital has been considered the fountainhead of economic growth, significantly determining a nation’s innovative capacity. Liu and White ( 1997 ) have emphasized that innovation is driven by both absorptive capacity and new knowledge sources, with R&D personnel serving as a crucial manifestation of the former (Liu and White, 1997 ). Studies by Suseno et al. ( 2020 ), Lao et al. ( 2021 ), and Wen et al. ( 2023 ) have elucidated the innovation effects of high-level human capital from different perspectives. Thirdly, the innovation effects of digital (information) infrastructure are primarily realized through two mechanisms (Liu and Li, 2021 ; Zhang et al., 2022 ; Guo and Zhong, 2022 ; Ma and Lin, 2023 ; Tang and Zhao, 2023 ): (1) by reducing information asymmetry; (2) by breaking through administrative boundaries and geographical distances, facilitating information exchange and knowledge spillover among innovative entities.

Given the aforementioned statistical results and theoretical foundation, we are justified in deducing that NSC can promote the development of knowledge innovation through the impact on regional financial resources, human resources, and material resources, and the basic effect in hypothesis 1 has been tested.

Test of network effect and technology effect

Following the same methodology as the basic effect test, Table 6 reports the regression results of network effect and technology effect, using column (4) in Table 3 as the benchmark test (first step).

On the one hand, taking Centrality_Degree and Centrality_Closeness as dependent variables, it can be seen from columns (11) and (13) in Table 6 that the regression coefficients of NSC are both significantly positive at the 1% level (9.069/8.680), indicating that the construction of NSC has improved the centrality of city in the regional research cooperation network, that is, promoting the embedding of the city in the network structure. By relying on the supercomputer comprising thousands of processors and extending the development of new digital technologies such as big data, artificial intelligence, and cloud computing, NSC construction has not only expanded the city’s computing power services but also enhanced the region’s information and knowledge processing capabilities. Other cities are also more willing to establish cooperative relationships with NSC cities, thus promoting the embedding of the regional innovation network. Columns (12) and (14) in Table 6 report the regression results for the third step of the network effect, where Centrality_Degree , Centrality_Closeness , and NSC are simultaneously included in the regression equation. The estimated coefficients of the mechanism variables are both significantly positive at the 1% level (0.736/0.528). The promotion effect of NSC on knowledge innovation remains significant, and the estimated coefficients decrease from 8.934 to 2.226 and 4.329. In accordance with the social network theory, disparities in the positioning of individuals within a network can significantly influence the quantity and quality of information and resources they acquire, which leads to variations in innovative performance. Existing literature, based on diverse samples, has unveiled the augmented innovative performance associated with higher centrality in networks (Han et al., 2021 ; Wang et al., 2019 ). When entities occupy more central positions, they can engage in multidimensional technical collaborations and knowledge exchanges with various members. This enhances their capacity to absorb, transform, and reconfigure knowledge.

This result confirms that the construction of NSC can enhance the level of knowledge innovation by promoting the city’s embedding in the scientific cooperation network, thereby verifying hypothesis 2.

On the other hand, column (15) in Table 6 presents the second step estimation result of the technology effect mechanism. It shows that the estimated coefficient of NSC is significantly positive at the 1% level (0.091). After including Innova_effi and NSC in the regression equation, the estimated coefficient of the mechanism variable remains significantly positive at the 1% level (55.269). The absolute value of NSC ’s estimated coefficient decreases from 8.934 to 3.884 while still being significant. Indeed, the significance of knowledge production efficiency in innovation is primarily manifested through two dimensions. Firstly, R&D activities entail considerable risks and uncertainties, and high-efficiency aids in mitigating the costs associated with the knowledge innovation process. Secondly, there is the constraint of limited R&D resources. High efficiency implies a more effective utilization of funds and human resources, enabling the realization of a greater quantity and higher quality of innovative outcomes with the same inputs. Existing studies not only reveal the positive impact of technological efficiency enhancement on innovation performance but also underscore the crucial roles played by management efficiency and resource allocation efficiency in the innovation process (Bughin and Jacques, 1994 ; Hu and Chen, 2016 ; Yang et al., 2022b ).

The theoretical analysis and statistical results above indicate that NSC promotes the development of knowledge innovation by improving regional innovation efficiency, which confirms the technology effect (hypothesis 3). By enhancing the city’s computing power and allocation efficiency, NSC not only shortens the cycle of knowledge innovation and reduces its costs, but also optimizes the allocation of S&T resources, achieving scientific decision-making for urban innovation development.

Further analysis

Policy spillover.

Geographical proximity is not the only pathway that affects the diffusion of knowledge. With the rapid development of digital technology and the increasing improvement of infrastructure, the cross-regional flow of R&D elements makes the spatial connection of different cities closer. This study further examines the policy spillover of NSC from two aspects: cooperation proximity and digitization proximity. To address the problem of unclear coefficient economic implications, LeSage and Pace ( 2009 ) proposed the use of the partial derivative matrix method to divide the impact of the independent variable on the dependent variable into direct effect and indirect effect. In this study, the impact of the local NSC on knowledge innovation is considered a direct effect, while the impact of other regional NSCs on local knowledge innovation is regarded as an indirect effect.

The estimated results are shown in Table 7 , revealing that regardless of whether the geographical proximity matrix, cooperation proximity matrix, or digitization matrix is utilized, the estimated coefficients of NSC in both direct and indirect effects are significantly positive, confirming the existence of policy spillovers of NSC under different matrices (H4a is verified). This indicates that, in addition to radiating to geographically adjacent areas, NSC provides computing services to closely connected partners, thereby promoting local knowledge innovation development. Furthermore, the policy spillover effect of the supercomputing center is more effective when cities have comparable levels of digitization. The above results verify the policy spillover at the cooperative dimension, indirectly indicating that if the digital technology level of the city is limited, local knowledge innovation development is difficult to benefit from the NSC construction in the advanced areas. Considering the size and significance of the indirect effect coefficient, it can be seen that under the geographic and digitization matrices, the indirect effect of NSC is stronger, reflecting that both geographic distance and economic (digitization) distance are still the primary factors influencing policy spillover.

Given the existence of policy spillover in NSC, we construct β-convergence model based on the way of Sala-i-Martin ( 1996 ), examining the impact of NSC on regional knowledge innovation convergence. As depicted in Eq. ( 5 ) below, where \(L.{\mathrm{ln}{KI}}_{{it}}\) represents the lagged term of knowledge innovation, and \(D.{\mathrm{ln}{KI}}_{{it}}\) is the first-order differencing term for it. Our focal point lies in the alteration of \({\beta }_{0}\) before and after the inclusion of NSC . Should it be statistically significant (less than zero), notably increased in absolute value, it would signify that the establishment of NSC contributes to fostering inter-regional convergence in knowledge innovation.

The regression results are depicted in Table 8 . Whether using spatial econometric models or ordinary least squares, the coefficient of the lagged term L.lnY for knowledge innovation is significantly negative at the 1% level, implying that, after taking into account factors such as per capita GDP, comprehensive growth rate, and industrial structure, the latecomer regions have a higher knowledge growth rate than the knowledge-intensive regions. Columns (24) and (26) present the regression results after incorporating NSC , where the coefficient of L.lnY remains directionally and significantly unchanged, with only a slight increase in absolute value from 0.632 and 0.630 to 0.634 and 0.652. This indicates that although NSC can achieve policy spillover through geographical proximity, cooperation proximity, and digitization proximity, the impact is not sufficient to drive regional knowledge innovation convergence. Hypothesis 4b is not significantly supported by the results.

Heterogeneity analysis

The antecedent findings corroborate the knowledge innovation effects of NSC and unveil its primary mechanisms. Nevertheless, this impact may vary due to differences in urban knowledge orientation and scientific environments. This paper examines this heterogeneity in three distinct ways.

In comparison to research in aerospace, meteorology, engineering simulation, and other fields, basic scientific research may be less affected by NSC, despite collaborative research in areas like new energy, new materials, particle-liquid simulation, and condensed matter physics within Chinese NSCs. In erecting a single NSC in China, the government typically invests tens of millions of dollars at least, aspiring that NSC advancements will tackle tangible societal challenges and propel economic innovation. Yet, per information gleaned from prominent Chinese supercomputer portals and media coverage, the utilization of supercomputing in the realm of Mathematics seems relatively rare. Thus, based on “Research Area” information from the WoS database, scientific publications affiliated with each city under “Mathematics” are obtained. Firstly, cities are then classified into high-percentage groups (City_B) and low-percentage groups (City_NB) based on the proportion of publications (see “Critical value” in Table 9 , where 0.03 signifies that if the city’s mathematics publications exceed 0.03 in proportion, it is categorized as “City_B” with a value of 0). Despite Bdiff command tests indicating no significant differences in NSC coefficients between the two groups, the absolute value of the NSC coefficient in the “City_NB” group is slightly higher than that in the “City_B” group.

Secondly, this paper constructs the interaction term ( NSC×Basic ) to examine the moderating effect of urban knowledge orientation on the impact of NSC, as shown in columns (31) and (32) in Table 9 . The estimated coefficients of the interaction term ( NSC×Basic ) are significantly negative at the 1% level (−2.956/−2.573), indicating that the knowledge innovation effects of NSC tend to be lower in cities with a high proportion of “Mathematics” publications.

Thirdly, heterogeneity tests conducted through grouping or moderating effects cannot be precise for each individual and often pale in comparison when dealing with fewer groups. This paper further employs the synthetic difference in differences proposed by Arkhangelsky et al. ( 2021 ) to estimate the individual treatment effects (ITE) of cities. The estimation results in Table 10 provide the average treatment effects, T -values, and 95% confidence intervals for each city in the treatment group. It is noteworthy that only Shenzhen and Guangzhou exhibit significant treatment effects (8.938/3.685), and the Basic values in these two major cities are lower than the mean of the treatment group. Among them, Shenzhen stands out as a typical application innovation-oriented city (while also facing criticism for lacking a layout in basic research).

Robustness test

Parallel trend test.

The selection of an NSC site requires consideration of both the economic foundation of the city itself and its radiating influence in the region. Typically, the chosen city already possesses a relatively advanced knowledge base. To ensure the SDID model satisfies the “parallel trend” assumption prior to shock, we further examine the trend changes in both NSC and non-NSC cities. The equation is set as follows:

The study focuses on the coefficient β 1 of the interaction term between the time dummy variable and the NSC city dummy variable (if the city has NSC, the value is 1; otherwise, it is 0), as shown in Fig. 3 . The observation of the treatment effect can be divided into two stages. The first stage is before 2009, where it can be observed that the estimated coefficients of the interaction term are not significant, indicating no statistically significant differences in knowledge innovation changes between the treatment and control groups before policy implementation. The second stage is from 2009 to 2020, during which the policy treatment effect began to emerge in the second year of NSC construction and has been increasing year by year. The model satisfies the pre-assumption of “parallel trends,” while also presenting the dynamic changes of the treatment effect.

Following the construction of NSC, the estimated coefficients gradually become significant, and the effect of policy begin to manifest. This also indicates that the DID model satisfies the assumption of pre-parallel trends.

Endogenous processing

Due to the potential inclination of NSC construction sites toward cities with superior digital infrastructure, these urban centers often exhibit a heightened level of knowledge production. To mitigate the bias stemming from sample selection, reverse causality, and omitted variables, we try to address this issue under both OLS and spatial econometric models:

On the one hand, regarding the NSC as an endogenous variable, this study selects the per capita-fixed telephone ownership in 1984 ( FT ) (Li and Wang, 2022 ), relief degree of land surface ( Rdls ) (Zhang et al., 2022 ), and the frequency of digital economic policy terms ( FDEPT ) (Jin et al., 2022 ; Tao and Ding, 2022 ) as instrumental variables. These are chosen as instruments based on their correlation with the endogenous variable and independence from the error term. The historical level of information infrastructure influences the subsequent development of digital technology in the region; computing efficiency depends on data transfer speed (connectivity), which is influenced by the Rdls of the city (the cost and difficulty of constructing digital infrastructure); whether a region is selected as an NSC construction city is also influenced by the degree of emphasis on digitization in public sector policies. In terms of exogeneity, historical variable represented by FT and the geographical variable represented by Rdls have exclusive characteristics. Given that FT and Rdls are both cross-sectional data, this study adopts the approach outlined by Nunn and Qian ( 2014 ). We multiply the previous year’s nationwide total of internet and mobile phone users by FT , while Rdls is multiplied by the time trend terms.

The validity tests for the instrument variable selection are presented in Panel A of Table 11 , where the Kleibergen-Paap rk LM statistics are significant at the 1% level, F -values are all greater than 10, both Cragg-Donald Wald and Kleibergen-Paap rk Wald statistics exceed the critical values of the Stock-Yogo weak ID test (10% maximal IV size). This suggests that the three types of instrumental variables do not suffer from “under-identification” and “weak instrument” problems. Columns (33), (34), and (35) show the regression results for the first stage, indicating that the estimated coefficients of FT and PWF are significantly positive at the 1% level (0.249/5.016). It implies that the historical level of information infrastructure and the policy attention of the public sector to digitization indeed have a positive impact on whether a city is selected as an NSC. In contrast, the estimated coefficient for Rdls is significantly negative at the 1% level (−0.015), reflecting that higher Rdls do hinder a city from being selected as an NSC city. The results of the second-stage regression are shown in columns (36), (37), and (38), with NSC estimated coefficients all being significantly positive at the 1% level (69.125/37.768/20.310).

On the other hand, we try to address the endogeneity issue in the spatial econometric model in three different ways. (1) Dynamic SDM. Compared to static models, the dynamic SDM is advantageous in its more comprehensive consideration of time factors. This study sequentially includes the time-lagged dependent variable (dlag_1), the space-time-lagged dependent variable (dlag_2), and both of them (dlag_3) as explanatory variables in the regression model. The results shown in columns (39), (40), and (41) of Table 12 indicate that the estimated coefficients of the NSC are significantly positive at the 1% level (98.422/8.886/99.982). (2) Generalized spatial two-stage least squares method. Following the approach of Wang et al. ( 2022 ) we select the independent variable and its spatial lag term as instrumental variables. The regression results are shown in column (42) of Table 9 . Whether using first-order (1st order), second-order (2nd order), or third-order (3rd order) lagged independent variables (present only the results for the 1st order), the estimated coefficients of the NSC are also significantly positive at the 1% level. (3) Incorporating instrumental variables like FT , Rdls , and FDEPT into the G2SLS model, the results of the subsequent regression demonstrate that the NSC estimated coefficients still remain significantly positive at the 1% level (68.396/31.614/23.229).

The above outcomes indicate that potential endogeneity concerns do not significantly affect the validity of the baseline results.

Placebo and PSM-SDID

Building upon parallel trend tests, this study employs counterfactual analysis to further perform placebo analysis. By changing the construction time of the NSC and investigating the treatment effect determines whether the improvement in urban knowledge innovation is caused by the NSC. If the coefficient is significant, it suggests that the improvement of urban knowledge innovation level may not be caused by NSC, and the conclusion is not robust. Referring to Gao and Yuan’s research ( 2020 ), we only retain the samples from the period between 2000 and 2008, estimating them again by respectively moving the policy time forward one period (2008), two periods (2007), and three periods (2006). The results are shown in columns (46), (47), and (48) of Table 13 , with the NSC being insignificant, indirectly proving that the improvement in knowledge innovation level is attributed to the NSC. Moreover, this study employs the PSM-SDID method to conduct robustness checks on the original model, to overcome potential endogeneity issues caused by selection bias, and to enhance the accuracy of causal identification results. Using the year-by-year method to perform kernel matching, Econ , Industry_sec , n  +  g  +  δ , and the proportion of fiscal S&T expenditure to GDP are selected as a covariate. The standardized bias of each covariate after matching is less than 20 percent. Considering the requirements of spatial econometric models for balanced panel data, the samples with missing data in the year are removed, and ultimately, 714 samples are retained. As shown in columns (49), (50), and (51) of Table 13 , whether the spatial econometric model is adopted or not, the estimation results are consistent with the benchmark regression results, indicating the robustness of the positive impact NSC has on urban knowledge innovation obtained in the previous analysis.

Replacing the estimation method, variable, and sample

(1) Change the estimation method. Given that some cities have a value of zero for knowledge innovation, which accounts for a certain proportion of observations, the dependent variable being clustered on the left side of the value range may lead to biased estimation. Therefore, Tobit and negative binomial models are used to re-estimate the results. As shown in columns (52) and (53) of Table 14 , the estimated coefficients of NSC are significantly positive at least at the 1% level (8.958/0.236), indicating that the benchmark test results are not significantly affected by the structural characteristics of data. (2) Replace the dependent variable. Firstly, the knowledge innovation variable in our study is constructed by taking the ratio of urban S&T publications to the number of permanent urban residents. We replace permanent residents with urban employees to construct a new knowledge innovation variable and conduct another estimation. The estimated coefficient of the NSC is also significantly positive at the 1% level (25.857). Secondly, by using the number of highly cited papers in urban as the dependent variable, the estimated coefficient of NSC is significantly positive at the 1% level (0.491). The treatment effect of the policy remains robust, and both the quantity and quality of knowledge innovation are measured, achieving cross-validation. (3) Change sample. Considering that the small number of treatment groups in the sample may cause bias to the estimation, this paper deals with it by changing the sample in two ways: on the one hand, the number of samples (control group) is deleted. The study only retains 35 large and medium-sized cities in China and deletes samples of other cities for re-estimation. On the other hand, change the sample dimension. We raise the dimension to inter-provincial (31 provinces, municipalities, and autonomous regions), and Tianjin, Guangdong, Shandong, Jiangsu, Hunan, and Henan are respectively used as treatment groups (the data on the publication of S&T in the provincial area comes from the “China Science and Technology Statistical Yearbook”). Table 14 shows the SDID regression results (columns (56) and (57)), and the estimation coefficients of NSC are all significantly positive (3.020 /3.338), indicating that the previous research results are very robust.

Conclusion and policy implications

Discussion and conclusion.

As an important component of the national innovation system, LSRIs possess the capability to explore the unknown world, discover natural laws, and achieve S&T outputs. Existing research has revealed the impact of LSRIs on socio-economic development (especially in S&T innovation) from different perspectives (Marcelli, 2014 ; Michalowski, 2014 ; Qiao et al., 2016 ; Beck and Charitos, 2021 ), and theoretically explored the various dimensions of LSRIs’ scientific effect (Michalowski 2014 ; Qiao et al., 2016 ). However, there are two primary challenges in evaluating the impacts of construction: firstly, insufficient examination of the link between LSRIs and regional knowledge production; secondly, limited testing conducted within a causal inference framework. This study examines the impact of LSRIs on regional knowledge innovation with the backdrop of the Chinese NSC. The research results reveal the positive significance of this effect to a certain extent, directly confirming the scientific effect or S&T advancement effect of LSRIs mentioned in existing literature (Michalowski 2014 ; Qiao et al., 2016 ). Through mechanism testing, the identification of network effect (Lozano et al., 2014 ; Qiao et al., 2016 ; D’ippolito and Rüling, 2019 ; Beck and Charitos, 2021 ), capability cultivation (Michalowski 2014 ; Qiao et al., 2016 ), and clustering effect (Qiao et al., 2016 ; Beck and Charitos, 2021 ) is indirectly achieved. While increasing regional scientific financial, human, and material resources, LSRIs also contribute to the embedding of cities in regional innovation networks and the efficiency of utilizing innovation resources. Qiao et al. ( 2016 ) considered that the network effect is an important mechanism for LSRIs to interact with science stakeholders and strengthen scientific cooperation. Based on the co-publication of scientific publications, this study extends such network effects to the scientific cooperation connections established between cities. Other cities are more willing to establish cooperative relationships with NSC cities because they can benefit from computing power services. The improvement of data processing capabilities will also alleviate information overload problems and stimulate NSC cities to actively integrate into the innovation network. Some studies have mentioned the function of LSRIs in technology promotion and knowledge diffusion (Beck and Charitos, 2021 ; Scarrà and Piccaluga, 2022 ). Based on the spatial econometric model, our research results reveal the spillover effect of LSRI implementation, and this diffusion mechanism exists on multiple levels, including geographical proximity, cooperation proximity, and digitization proximity.

As a new productivity in the digital economy era, computing power plays an important role in promoting S&T progress, industry digital transformation, and economic and social development. NSC has the dual attributes of LSRI and digital infrastructure. Therefore, the research findings of this study also complement the literature regarding how digital infrastructure impacts the growth of innovation. Previous research has examined the impact of digital infrastructure on productivity and innovation from different dimensions including region and enterprise (Cardona et al., 2013 ; Balcerzak and Bernard, 2017 ; Zhou et al., 2021 ; Zhang et al., 2022 ; Tang and Zhao, 2023 ). However, the definition of digital infrastructure is mainly focused on network and communication infrastructure, lacking involvement in computing power. This paper provides direct evidence of how computing infrastructure impacts regional knowledge. NSC supplies high-performance computing services for scientific research, improving research and development efficiency, and shortening the output cycle of scientific research results (Marcelli, 2014 ). Moreover, it drives the development of new digital technologies represented by 5G, big data, cloud computing, and artificial intelligence, promoting the development of regional knowledge innovation. In addition, NSC is a national-level computing power hub established by the government based on urban innovation ecosystems in specific geographic locations, undertakes multiple missions of promoting local digital innovation, and accelerating knowledge spillover. Therefore, NSC can also be regarded as a place-based innovation policy. The research findings of this study reveal the significant impact of the intervention on local knowledge production. However, the driving effect of local knowledge growth on the convergence of regional innovation is limited, which is different from the evaluation results of other place-based innovation policies like “National Innovative City” and “urban cluster” (Tang and Cui, 2023 ; Gao and Yuan, 2020 ). The main reasons could be that the number of cities with NSC is still limited, and the construction of provincial and even more microscopic-level supercomputing centers has not been considered, which may lead to an underestimation of the radiation effect from the center. It is noteworthy that, akin to certain assessments of policy or digital innovation effects (Zhou et al., 2021 ; Liu and Li, 2021 ; Zhang and Wang, 2022 ; Tang and Zhao, 2023 ), our study encapsulates the inter-regional heterogeneity of NSC knowledge innovation effects. Diverging from existing research that relies on economic or geographical heterogeneity analysis (Yang et al., 2021 ; Gao and Yuan, 2020 ; Chen et al., 2023b ), our findings further unveil potential disparities in NSC innovation effects due to differences in urban scientific knowledge development emphasis.

In summary, the findings of this study can be distilled into the following key points:

NSC construction promotes local and surrounding area knowledge innovation.

The main mechanisms by which NSC promotes regional knowledge innovation include the increase in fiscal investment and talents in S&T (basic effect), the improvement of digital infrastructure (basic effect), as well as the enhancement of urban network centrality(network effect), and innovation efficiency(technology effect).

Geographical proximity, cooperation proximity, and digitization proximity constitute the main channels of policy spillover.

NSC has not shown a significant promoting effect on regional innovation convergence, and the radiation influence needs to be further improved.

Knowledge innovation effects of NSCs vary based on differences in urban knowledge orientation and scientific environments, with the treatment effects being notably pronounced in application innovation-oriented cities, exemplified by Shenzhen.

Policy implications

Firstly, considering the facilitating role of NSC in scientific knowledge production, it is necessary to enhance the supporting effect of LSRIs in scientific basic research and technological application research. While improving R&D efficiency, releasing the attraction of the large-scale scientific projects to innovative factors, and increasing the investment in S&T and the number of R&D personnel, improving urban digital infrastructure, and promoting the deep embedding of cities in scientific research collaboration networks.

Secondly, The study emphasizes the need to strengthen the policy spillover effect through various channels. This can be achieved by developing a city cluster strategy that coordinates the collaborative network of computing power within and around urban areas such as the Beijing-Tianjin-Hebei, Yangtze River Delta, and Greater Bay Area regions. For cities that have not yet established NSC, efforts should be made to optimize regional digital infrastructure and actively integrate into inter-regional cooperation networks, in order to create a favorable environment and basic conditions for cross-regional computing power scheduling, as well as to expand knowledge spillover in digitization and cooperation proximity.

Thirdly, given the weak promotion of NSC on regional knowledge innovation convergence, in the future, to strengthen the role of the national computing power hub as a connector and coordinator in the overall layout of the national computing power network, the computing infrastructure layout should be systematically optimized, with a focus on guiding the reasonable hierarchy for the layout of general data centers, supercomputing centers and intelligent computing centers. In this process, addressing the “computing power island” problem and expanding the influence of the center is an urgent issue that requires providing inter-city computing power collaboration and on-demand scheduling solutions.

Fourthly, the findings of this study reveal that the implementation of a place-based innovation policy, through the strategic establishment of NSCs in different regions, can effectively facilitate the growth of local knowledge creation. However, in order to achieve inter-regional convergence of knowledge, a concerted effort to refine inter-regional coordination mechanisms needs to be undertaken, simultaneously with the expansion of the centers. The limited yet positive contributions of NSCs also furnish valuable insights for other countries in the construction of LSRIs, digital infrastructure development, and implementation of place-based innovation policies. Particularly, in the selection of NSC locations, there should be consideration for regional disciplinary emphasis and the innovation environment, coupled with increased support for fundamental research.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This work was supported by the National Natural Science Fund of China (Grant No. 71810107004).

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Yang, H., Liu, L. & Wang, G. Does large-scale research infrastructure affect regional knowledge innovation, and how? A case study of the National Supercomputing Center in China. Humanit Soc Sci Commun 11 , 338 (2024). https://doi.org/10.1057/s41599-024-02850-8

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Making a Research Infrastructure: Conditions and Strategies to Transform a Service into an Infrastructure

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Benedikt Fecher, Rebecca Kahn, Nataliia Sokolovska, Teresa Völker, Philip Nebe, Making a Research Infrastructure: Conditions and Strategies to Transform a Service into an Infrastructure, Science and Public Policy , Volume 48, Issue 4, August 2021, Pages 499–507, https://doi.org/10.1093/scipol/scab026

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In this article, we examine the making of research infrastructures for digital research. In line with many scholars in this field, we understand research infrastructures as deeply relational and adaptive systems that are embedded in research practice. Our aim was to identify the relevant context factors, actor constellations, organizational settings, and strategies which contribute to the evolution of a basic service into an actual infrastructure. To this end, we conducted thirty-three case studies of non-commercial and commercial research services along the research life cycle. By examining how these services emerge, we hope to gain a better understanding of the conditions and strategies to transform a service into an infrastructure. We are able to identify competitive disadvantages for publicly financed infrastructure projects with regard to the mode of implementation and the resources invested in development and marketing. We suggest that the results of this study are of practical relevance, especially for individuals, communities, and organizations wanting to create research infrastructures, as well as for funders and policy makers wanting to support innovative and sustainable infrastructures.

Digital communication technologies have proved instrumental in changing practices across all sectors of society, including academia. The hope of many researchers and science policy makers alike is that the Internet will help foster scientific progress and ultimately to make science more open, that is more inclusive, accessible, and transparent (cf. Fecher and Friesike 2014 ; Heck 2021 ). However, realizing efforts such as this require concrete policy initiatives behind them, if they are to endure and become part of everyday research practice. To date, many policies tend to focus on getting the technical aspect of research infrastructures off the ground, such as the development of major scientific equipment, sets of archival or scientific data, or communication and computing networks ( European Commission, 2016 ). As a result, we have seen a plethora of services emerge in recent years, which stand as a testament to the firm belief in scientific progress due to technology. While these are a valuable step in trying to meet new user and stakeholder needs and thereby integrate into the research life cycle (and, in some cases, attempt to reconfigure it), we argue in this article that there is more to research infrastructures than technical black boxes.

Infrastructure studies offer a fruitful perspective from which to study how technical innovations might generate effects which loop back upon the social organization of science. Scholars in this field largely agree that only when a technical service is embedded in practice, when it becomes ‘invisible’ ( Star and Ruhleder 1996 ; Bowker and Star 1999 ), can it be considered part of an infrastructure. In this understanding, infrastructures are much more than the technical assemblage of things; only when these are part of practice, can they be considered part of the infrastructure. Bowker and Star (1999) refer to the depths of interdependence between the technical networks and the real work of knowledge production as ‘infrastructural inversion’ and suggest that infrastructures become examinable, when they break down. In this light, the transformative potential of the Internet on scholarly practice can be seen as an ongoing irritation for routinized academic work, which offers us an opportunity to study changes in scholarly practice through the infrastructural lense ( Kaltenbrunner 2015 ).

In this article, we present the results of an empirical study on the emergence of research infrastructures for digital science that we conducted as part of a research project funded by the German Federal Ministry of Education and Research (BMBF). In particular, we are interested in the relevant environmental (i.e. legal, political, and social) factors for research services (RQ1), the strategies services apply to engage users and stakeholders (RQ2), and the typical organizational characteristics (i.e. team constellation, workflows, and financing) that services feature (RQ3). To approach these questions, we conducted thirty-three case studies of emerging services along the research life cycle between March and December 2018. We used desk research and semi-structured interviews with representatives of these services (mostly founders, CEOs, and project leads). Our results shed light on the motivations and logics behind infrastructure development and the interdependencies between new technical services and academic knowledge production. We are able to identify competitive disadvantages for publicly financed infrastructure projects with regard to the modes of implementation and the resources invested in development and marketing. The results of this study are of practical relevance, especially for persons and organizations which want to create and sustain research infrastructures and for funders and policy makers who aim to create the conditions for research in the twenty-first century.

2.1 Defining research infrastructures

For the purposes of this article, it is necessary to review the scholarly discourse on infrastructures and to derive a robust definition for an empirical investigation. To this extent, we conducted an extensive literature review drawing from infrastructure and information studies (see Online Appendix Table 1 ).

We find that there is a consensus in the scholarly discourse that infrastructures go beyond the pure material framework and also take into account social and environmental factors. Bowker and Star (1999) understand an infrastructure as a practical match among routines of work practice, technology, and wider-scale organizational resources. In their understanding, infrastructures are sunk into other structures of social arrangements and technologies and support communities of practice (cf. Bowker and Star 1998 ). In that line, Wouters (2014) defines infrastructures as a routinized and relational set of human interactions that are multilayered and cannot be constructed top-down. This echoes the work of Pollock and Williams (2010) who argue that infrastructures should be viewed iteratively over time, as entities with their own biographies and which only exist in social contexts. The bottom-up nature of infrastructures is further explored by Blanke and Hedges (2013) who argue that such an understanding is essential if an infrastructure is to adequately meet the needs of its users. Edwards (2013) describes infrastructures as ecologies or complex adaptive systems that incorporate technological standards, social practices, and norms. Similarly, Hanseth et al. (1996) propose that infrastructures rely on a degree of standardization and compatibility if they are to function effectively (see also Larkin 2013 ). Drawing on Strauss (1985 , 1988 ), Kaltenbrunner (2015) describes infrastructures as a result of articulation work, that is the activity of meshing distributed elements of labor in cooperative settings. He differentiates the production task (e.g. a research report) from the articulation work (i.e. everything that is necessary to write the report). These settings, as previously described by Schmidt and Bannon (1992) , are increasingly distributed, thus requiring the kinds of cooperative, digitized support infrastructures that form the basis of this study.

We suggest that these general conceptions of infrastructures can be transferred to research infrastructures. Drawing from this, we proceed from an understanding of research infrastructures as deeply relational and adaptive systems where the material and social aspects are in permanent interplay. They are embedded in the social practice of research and influenced by environmental factors. This allows us to consider the examined services as infrastructures in the making, that is they are not (yet) part of research practice but try to become part of it, and informs our central research interest: by examining how these services emerge, we hope to gain a better understanding of the conditions and strategies to transform a service into an infrastructure.

2.2 Conceptual framework

Three conceptual dimensions appear particularly relevant in the context of this study and for answering our three research questions:

Environmental perspective, that is the ecology in which services operate.

This conceptual dimension relates to the first research question and thus which and to what extent environmental factors play a role in the development of an infrastructures for digital science. As adaptive systems, it can be assumed that research infrastructures do not emerge without context and are indeed influenced by environmental factors. Here, we distinguish between legal norms (e.g. with regard to data protection) as well as societal and political discourses (e.g. science policy developments) with regard to the influence of digitalization on science.

Social perspective, that is the practice that services try to penetrate.

This conceptual dimension relates to the second research question, that is the strategies services apply to engage users and stakeholders. Services must be embedded into the social practice of research in order to be part of the research infrastructure. In this context, two large (and occasionally overlapping) groups of social actors appear crucial to us. These are the actual users (i.e. people who use a service) and relevant stakeholders (i.e. people who do not use a service but are directly relevant to its provision). For example, repositories are used by researchers (i.e. they are the users), but they are funded by research funders and hosted by libraries (i.e. they are stakeholders). We assume that both groups are relevant for a service to become part of practice. Empirically, we are interested in what practical problems a service wants to solve (i.e. motivation), which users and stakeholders they address and what strategies they employ to engage them, i.e. to become part of the practice.

Organizational perspective, that is the resources that services have to adapt.

This conceptual dimension relates to the third research question, that is the organizational characteristics that services feature. Taking the perspective of technical services, we are interested in the organizational capacities that a service has with regard to the team constellation, modes of implementation of changes, as well as the financial resources. Thereby, we assume that the interplay between the material and the social does not only relate to the relationship between the service and its (external) users and stakeholders but also to the internal, social, and material, capacities.

This study is part of the BMBF-funded research project DREAM (Digital Research Mining), which deals with infrastructures for digital science (i.e. scholarly practices that rely on digital resources). 1 The aim of this study was to better understand the conditions and strategies to transform a service into an infrastructure. We assume that the transformative potential of the Internet makes it possible to study infrastructures for scholarly practice insofar as new services challenge existing infrastructures and seek to become part of the infrastructure themselves.

To this end, we conducted thirty-three case studies of non-commercial and commercial research services along the research life cycle between March and December 2018. We used a purposeful, theoretical sampling, guided by three criteria: size, source of funding, and functionality. Regarding functionality, we chose cases that can be assigned to different phases of the well-established research cycle (cf. Wilkinson 2000 ; Humphrey 2006 ). This is to ensure that sufficient cases are included in our analysis for all practices and phases in a typical research project. Accordingly, we differentiated five broad phases (think and plan; discover; gather and analyze; write and publish; share and impact). Many services in our sample cover more than one phase. For instance, the service Knowledge Unlatched offers features for discovering and publishing. We approximated the size of a service by the numbers of employees indicated in the interviews and other available information such as profit and number of users. It was important to include both large and small services in order to better assess the impact of organizational resources on infrastructure development. Similarly, it was important to include both commercial and publicly financed services, as the two are subject to fundamentally different operational conditions (e.g. accountability to a research funder versus accountability to shareholders). It has to be said that many services have mixed business models. For instance, it is quite typical that services that receive public funds also receive individual payments by customers. A table of the cases in our sample can be found in the Online Appendix Table 2 .

We conducted semi-structured interviews with representatives of the services (mostly CEOs, founders, or project managers). For the instrument, we converted the aforementioned conceptual categories into questions. This resulted in three topics:

Environment (i.e. relevant political and societal discourses, and legal frameworks),

Social practice (i.e. motivations, user, and stakeholder strategies), and

Organization (i.e. team constellation, business model, and technical implementation).

The personal interviews have resulted in rich, textual data for the comparative analysis. We used a word-exact transcription of the interviews for our qualitative content analysis (cf. Mayring 2004 ). To this extent, we proceeded from a rough, deductive framework informed by the aforementioned categories and research interests and refined the category system through multiple rounds of thematic coding and coder discussions. In order to establish inter-coder reliability, all interviews were analyzed by two coders, using MAXQDA. Not all interviewees agreed to allow us to use their institutions’ names or to publish the full transcripts. In these cases, we speak generally of ‘service + number’ and avoid identifiers in quotations. In general, the results will not refer to the interviewed persons by name, but to the services they represent.

Here, we present the main findings of our research, relating to (1) environment (i.e. relevant political and societal discourses, legal frameworks), (2) social practice (i.e. motivations, user, and stakeholder strategies), and (3) organization (i.e. team constellation, modes of implementation, and business model).

4.1 Environment

We defined the external context in which the services operate as their environment, which consists of the legal frameworks within which it may operate, as well as relevant political and societal discourses. How the service anticipates these influences its ability to become embedded in research practice.

4.1.1 Legal framework

When asked about which legal provisions are of relevance for running their service, the respondents largely referred to copyright, privacy, and standard licenses. The majority of codes refer to privacy regulations (forty codes), followed by copyright compliance (twenty-three codes), and references to standard licenses (seven codes). The core operational challenge here is presented by different national legal regimes, to which the services—most of which operate internationally—must respond. In addition, when it comes to copyright, services aim to keep the threshold for sharing material low and often try to avoid individual licensing solutions by using standard licenses (e.g. Creative Commons). In order to comply with this set of legal obligations, research services need to invest in monitoring, compliance, and implementation work, as the interview with Service 6, a service that offers a unique identifier for individual researchers, demonstrates:

We do a huge amount of work around privacy. Privacy regulations in every country are different. […] We’ve gone through an external privacy audit since 2013 to ensure that we’re meeting international standards. […] We are fully compliant with GDPR, we also have to look outside of Europe, what are the other privacy regulations that we need to comply with.

It is noteworthy that the three legal categories identified are central legal concerns for any web-based service (also in non-academic contexts). This reveals the digitally enabled nature of the observed services. As with other web services, a key challenge is anticipating different legal regimes.

Open science is the dominant theme that the respondents refer to when asked about the relevance of political developments to their services. At the time of the interviews, this largely referred to policies that advocate for open access and open data. Multiple respondents, for example, refer to transformative open access agreements (e.g. the German DEAL negotiations between major scientific publishers and consortia of scientific institutions) and data policies (e.g. FAIR). When it comes to the geographic scope, respondents refer mostly to national policies passed by governmental institutions or national funders (twelve codes), supra-national policies, such as those passed by the European Union (ten codes) and institutional mandates at the level of the library, university, or company (three codes). Many of the respondents state that they are monitoring policy developments closely, as these affect their business models. Here for example, a representative from Altmetric, a service that provides attention metrics for scholarly outputs, refers to developments in the realm of research evaluation.

We pay attention in the UK and Australia and Hong Kong, the Research Excellence Framework type of thing. So in Australia it is ERA, in the UK it is REF. So the guidelines on how to assess research. Obviously, we want to be the people you go to as a research admin at the university, to get the evidence to write this case and so you can get the money you deserve.

Most services align themselves to open science and the aforementioned dimensions (i.e. transparency, accessibility, and inclusivity). Some of the respondents even lobby for open science, which can be seen as creating favorable environmental conditions for the service and are thereby beneficial for becoming an infrastructure. This becomes obvious in the interview with the Directory for Open Access Journals (DOAJ), an online directory that indexes and provides access to open access, peer-reviewed journals:

We have been very much involved in pushing for open access policies, open access mandates in the European Union, for instance. At a national level we have been active behind the scenes lobbying for open access policies. We, together with many other organizations, have been quite successful in the last decade to motivate decision makers to go in the direction of open access and open science.

Interestingly, different understandings of open science stand out, especially when it comes to commercialization. Commercial services describe open science (implicitly and explicitly) as a business opportunity, whereas some non-commercial services articulate reservations about the commercialization of open science and even try to counter it strategically. This becomes obvious in the following quote from a representative of Dryad, a non-commercial repository for research data:

Universities and university libraries are concerned about commercial publishers and commercial entities sort of taking over the research infrastructure space. That’s part of what we are trying to combat with this new partnership with [name of a non-profit service] is how do we make nonprofit infrastructure that is more aligned with values of academia?

On the one hand, the results show how closely digital science is associated with Open Science by the interviewees. On the other hand, the results show a divergence in what is perceived as open science. In particular, non-commercial services are dedicated to the early activist understanding of open science as articulated in the Berlin Declaration in 2003 2 or the Budapest open access Initiative in 2002. 3 They often see open science as liberation from commercial interests. Commercial services, on the other hand, relate to open science as a practice (e.g. sharing data, making articles openly accessible) and not necessarily to the underlying ideologies.

4.2 Social practice

For services to become infrastructures, they must be embedded within the social practice of research. Accordingly, our aim here was to identify how exactly services intend to become part of research infrastructure, that is which motivations they have and what strategies they employ in order to engage users and stakeholders.

4.2.1 Motivations

We found that interviewees referred to eight different types of motivations. It is noteworthy that many of the motivations relate to the aforementioned open science dimensions, that is accessibility (e.g. access), inclusivity (e.g. dissemination and collaboration), and transparency (e.g. transparency). Beyond that, the motivations mirror efficiency (e.g. orientation) and research governance considerations (e.g. compliance, recognition, and efficiency). These motivations are further delineated in Table 1 .

Subcodes for ‘Motivation’.

You hand over the finished articles to publishers, including all rights. The publisher prints and distributes, so the rights are gone. The state basically paid twice, for paying the people who do the editing and for the libraries that buy the articles back. On the Internet, researchers have the opportunity to do this themselves. Service 1.

The motivations are of importance here because they show where the services see problems in current practice and thus how they justify their raison d’être. In many cases, services position themselves against other, already established services and in some cases even articulate a need to replace them.

4.2.2 Users and stakeholder strategies

Discovering how these motivations are translated into a strategy required identifying users and stakeholders and the activities designed to engage with them and meet their needs. It is important to distinguish between users and stakeholders when analyzing strategies, because user strategies tend to refer to technical adaptation needs (i.e. making a service useful), whereas stakeholder strategies tend to refer to outreach activities and customer relations (i.e. making a service accepted). Based on the responses, we identified eight user and six stakeholder groups (see Fig. 1 ). It became clear that researchers are by far the most important user group, bearing in mind that there are potential overlaps between the researchers and authors categories. The most important stakeholder groups are customers and data providers. The latter has potential overlaps with the other service category and shows how important other technical services and their APIs are for a service (e.g. Altmetric uses the Facebook and Twitter APIs to build an impact metric).

Users and stakeholders.

To a certain extent, the illustration of users and stakeholders provides a map of the relevant actors for digital research infrastructures. It shows that, in addition to the actors already expected, the platform and cloud services play a significant role in the making of research infrastructures and that services relate to other services outside of the academic sphere.

We identified eight strategies implemented by the services to adapt to user needs. We differentiated these between pull (i.e. when a service reaches out to users or monitors their behavior), push (i.e. when users reach out to the service), and dialog strategies (i.e. when user and service engage in a dialog)—see Table 2 .

Strategies to anticipate user needs.

If the customers are still interested, there will be another very intensive discussion, in which we really discuss all features and go into the contractual details, so that everything is really transparent and clear. The customers can then do a training session. We currently offer a basic training course, which ideally takes place before commissioning. As soon as the installation has gone online, after a while we offer intensive training in which individual questions can be answered. Service 3.

We find the stakeholder strategies particularly intriguing because they demonstrate what a service is doing in order to become interwoven with the research environment. We identified four different strategies to engage stakeholders (see Table 3 ).

Strategies to anticipate stakeholder needs.

Non-commercial services articulate problems in engaging stakeholders due to a lack of resources. Both for-profit and non-commercial services attempt to influence discourses in their favor (i.e. awareness work). The largest category, mediation work , shows that services go to great lengths in order to connect and translate between different stakeholder groups which are considered relevant to the service. These are generally users and customers (e.g. researchers and librarians at an institution), between a service and other services (e.g. to be technically connectable), and finally between the programmers and users (e.g. in order to match technical possibilities with user requirements). The latter illustrates the negotiation of the technically possible with the socially desired as indicated in the working definition for infrastructure. This becomes obvious in an excerpt from an interview with a representative from Knowledge Unlatched, a platform that supports open access to books:

I was with a team of very young developers, they all knew about the latest technologies and of course, they wanted to use these technologies, because that is most interesting for them […]. That was a challenge, because these designers and front-end developers; they all wanted to have some fancy moving buttons. When we asked librarians to login and to use it, they were like, what is this? They have no idea, give me an Excel sheet, and I’ll do it. Knowledge unlatched.

It becomes apparent that, in addition to the research communities as the biggest user group, other actors are of great relevance for the services—for example, because they guarantee the technical operation (e.g. data providers) or grant favorable institutional conditions (e.g. research institutions and research libraries). Furthermore, remarkable differences between commercial and non-commercial services can be seen, in that non-commercial or publicly funded services in particular articulate a lack of resources for outreach and implementation.

4.3 Organization

Here, we focus on the internal aspects of research infrastructures, in particular the roles that organizational design, team background, financing models, and technical adaptation play for the emergence of an infrastructure.

4.3.1 Team constellation

One of the problems we have had is that it is always hard to have sufficient developers. People have a lot of demands on a service naturally. They start using it, they like things, they have ideas for how they would like to innovate and it is hard to always have sufficient developers and to be able to offer people everything they would like. DCC.

In contrast to non-commercial services, for-profit entities described sales teams as an important part of their staff. These teams help the service to adapt by ensuring they are able to fulfill user and customer needs, thereby deepening their ability to embed themselves into the research practice. There are also indications that non-commercial services struggle to recruit staff who have technical expertise. This may be due to the fact that the salaries in non-commercial services (which are mostly based within scientific institutions) are typically lower than those in the private sector and that there are limited reputative gains for infrastructure work in academia.

4.3.2 Business models

Regarding the business models, we broadly distinguish between rather non-profit and profit-oriented services. Among non-profit services (sixty-six codes), we differentiated between those who received institutional funding (eighteen codes), public funding (seventeen codes), charged fees (five codes), accepted donations (eight codes), and services that were exclusively financed by the founder/s (four codes). Profit-oriented services (forty-nine codes) included subscription models and licensing (twenty-six codes), individual payments (five codes), and private investments (thirteen codes) as their funding sources. Most services have mixed funding models, or at least emphasized the intention to seek other/additional sources of funding.

[…] currently there’s just sort of the grant model, temporary funding that is designed to do some special project and then it ends and you’re left with no means for continuing the work DRYAD.

Access to initial seed funding was common to both types of entities, but while non-profits often received initial funding from public funders, profit-oriented services often relied on investments from external companies in their startup stages. Several services started with seed investment (e.g. Tetrascience) and angel investment or were part of a startup incubator. The issue of sustainability for services that receive public funding is notable. There appears to be a need for follow-up funding that has not been satisfactorily addressed by research funders. Strategic partnerships are another feature of the organizational design. In some cases, strategic partnerships led to services becoming merged (e.g. Sharelatex, Dryad, and the Dash platform) or were partly acquired by a larger service (e.g. figshare by Digital Science).

4.3.3 Technical implementation

We are able to differentiate two modes of technical implementation: phased and iterative implementation. Phased implementation (six codes) describes an approach that begins with the users, that is screening their needs and then building the service accordingly. Iterative implementation (fifteen codes) is a process whereby user needs are constantly screened and adaptations are continuously made. Generally, we observe that it was mainly non-commercial services which used the phased implementation approach, whereas for-profit services exclusively referred to iterative implementation. Below, in Table 4 , there are two example quotes, the first referring to iterative implementation, and the second to phased implementation:

Iterative implementation versus phased implementation.

We consider this to be an important result, since it seems to reflect the funding logic of many non-commercial services, who typically expect implementation in consecutive work packages, whereas for-profit services appear to have to search for exposure earlier and permanently. This, we suggest, may further limit the adaptability and thereby competitiveness of non-commercial services.

In our observations, it became clear that open science is the dominant discourse to which new online services for research refer. They use open science as an umbrella term to describe possible solutions to what they perceive as the shortcomings of the established system and infrastructures of the scholarly research life cycle, such as a lack of access to articles and the lack of recognition for alternative scholarly outputs (cf. Fecher and Frieske, 2014 ). What differs, however, are the services’ responses to this discourse: although open science was initiated as a movement against the commercialization of research, it has been anticipated as a business model by many of the commercial services we observed. Meanwhile, non-profit services see open science as a set of principles, which framed an activist approach to research support. This finding echoes critical voices that have pointed to the appropriation of open science by commercial players (cf. Mirowski 2011 ).

The differences between commercial services and non-profit services permeated almost every aspect of their responses to their environment’ (e.g. which public debates they participate in), how they engage with users and stakeholders, and how they implement changes. For instance, it is noteworthy that commercial services devote more resources to marketing and sales. Non-commercial services, on the other hand, articulate a lack of resources for marketing and sales. The distinctions between commercial and non-commercial services were also clear in the observations related to organization: Both types of services followed a fairly straightforward version of a decentralized digital service and both place similar importance on the need to hire staff with strong technical backgrounds. However, non-commercial services report that they do not have the resources to hire highly qualified programmers on a long-term basis. Further, non-commercial services often adopt phased implementation, possibly due to the funding logic of many public research funders. Commercial services generally adopt an agile implementation logic, possibly to be responsive to changing market needs.

Herein, we see a severe competitive disadvantage for non-commercial services. We suggest that there are three reasons for this: the first might have something to do with the phased implementation logic of public research funders, which restricts the capacity of a service to adapt to user needs. The second is a general lack of resources for hiring highly skilled staff, which puts non-commercial services at a disadvantage in a competitive market, and the third is a short-funding runway, which makes it difficult for non-commercial services to plan for future continuation. The implications of these three factors might be that in a competitive landscape, it is the commercial services, and their market-driven approach to open science, who have a better chance of embedding themselves in the research life cycle, and thereby co-shaping the scientific practices of the future.

In this research paper, we examined the making of research infrastructures for digital science, that is the relevant environmental factors, the strategies deployed to penetrate practice, and the organizational conditions necessary for a service to become part of a research infrastructure. We defined infrastructures as deeply relational and adaptive systems where the material and social aspects are in permanent interplay and which are influenced by environmental factors. The ways in which the services respond to these environmental factors and anticipate user and stakeholder needs create effects that might loop back into the overall social organization of science. It can be seen in our study that the services position themselves against shortcomings of the established infrastructures with regard to the access and transparency of research or the dissemination and curation of results online. In this regard, the study of emerging infrastructures might provide us with a glimpse into the future of an increasingly ‘open’ academic value creation.

At the same time, however, many services hold ties to established infrastructures, including mergers and acquisitions by the established publishers. In addition, the non-agile funding logic of public infrastructures and the limited financial possibilities of public institutions for highly trained staff could mean competitive disadvantages for publicly funded services. It therefore remains to be assumed that although the range of available services will change, the dominant players for research infrastructures may remain unchanged with digitization. This might explain why some scholars see open science as a neoliberal project in which market logics define the shape of research and non-lucrative services (e.g. for niche communities) are neglected ( Mirowski 2018 ). In this respect, the dependence on commercial research infrastructures seems to be reproduced for digital science. If there is an interplay between research policy developments and research infrastructures, and if public funding for infrastructure works do not take community needs sufficiently into account, then certain communities who coalesce around non-commercial services risk being left out of research policy debates. The risk of funding logics contradicting infrastructure logics, especially for digital services, increases as the relative dominance of commercial services grows (cf. Morris and Rip 2006 ; Fry et al. 2009 ; Lilja 2020 ). Although our study is limited in terms of the cases studied and the depth of survey, it gives reason to critically reflect on public research infrastructure investments, for instance by revising funding policies and increasing incentives for highly skilled non-research staff. It appears sensible to us to revive infrastructure research as a meta-scientific field of research especially now, in a time of transition to an increasingly digital ecosystem for scholarly work. This could help to ensure that public funds are used sustainably and moreover help to understand how possible futures of academic work might look like. Future, and in our eyes highly relevant, research questions could, for instance, concern the increasing interconnectedness and dependence on platforms, the long-term success of public infrastructure funding, and new governance models for critical infrastructures.

Supplementary data are available at Science and Public Policy Journal online.

Conflict of interest statement . None declared.

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Infrastructure →

• 02 Jan 2024
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• 21 Jan 2020
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• 11 Sep 2014

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• 03 Sep 2014

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• 26 Mar 2014

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• 31 Jan 2014

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• 01 Oct 2013

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• 27 Sep 2013

The Impact of Conformance and Experiential Quality on Healthcare Cost and Clinical Performance

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• 24 Jul 2013

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• 08 Jul 2013

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• 18 Apr 2013

The Impact of Pooling on Throughput Time in Discretionary Work Settings: An Empirical Investigation of Emergency Department Length of Stay

Improving the productivity of their organizations' operating systems is an important objective for managers. Pooling—an operations management technique—has been proposed as a way to improve performance by reducing the negative impact of variability in demand for services. The idea is that pooling enables incoming work to be processed by any one of a bank of servers, which deceases the odds that an incoming unit of work will have to wait. Does pooling have a downside? The authors analyze data from a hospital's emergency department over four years. Findings show that, counter to what queuing theory would predict, pooling may actually increase procesdsing times in discretionary work settings. More specifically, patients have longer lengths of stay when emergency department physicians work in systems with pooled tasks and resources versus dedicated ones. Overall, the study suggests that managers of discretionary work systems should design control mechanisms to mitigate behaviors that benefit the employee to the detriment of customers or the organization. One mechanism is to make the workload constant regardless of work pace, which removes the benefit of slowing down. Key concepts include: This research offers practical insights for workplace managers and health care policymakers. In workplaces where workers have discretionary control, the potential negative effects of designing pooled systems must be carefully considered. This has implications for designing and managing staffing structures and workflows, particularly in the context of service delivery organizations. Managers should consider implementing group incentives rather than individual incentives to motivate workers. This may encourage fast workers to reduce their speed just enough so that they will not negatively affect the productivity of others by over-utilizing shared resources. While workplaces often seek to incentivize workers through pay-for-performance programs that focus on individual productivity, a group-level approach may help counteract the negative effects that fast workers exhibit on overall productivity levels. In health care, emergency departments may benefit from implementing non-pooled work systems in which patients are assigned to a doctor-nurse team immediately upon arrival. Closed for comment; 0 Comments.

• 01 Mar 2013

Hurry Up and Wait: Differential Impacts of Congestion, Bottleneck Pressure, and Predictability on Patient Length of Stay

This paper quantifies and analyzes trends related to the effects of increased workload on processing time across more than 250 hospitals. Hospitals are useful settings because they have varying levels of workload. In addition, these settings have high worker autonomy, which enables workers to more easily adjust their processing times in response to workload. Findings show that heavy load plays a significant role in processing times. Congestion is associated with longer lengths of stay. More surprisingly, when there is a high load of incoming patients from a low pressure area (emergency medical patients), current hospital inpatients' stays are longer compared to when incoming patients are from a high pressure area (emergency surgical patients). Furthermore, high predictability of the incoming patients (e.g. scheduled surgical patients) is associated with shorter lengths of stays for the current inpatients than when the incoming patients are less predictable (emergency surgical patients). In this study, there was no decrease in quality of care for patients with shorter lengths of stay. Key concepts include: High congestion increases patients' length of stay by up to 0.81 days, which indicates inefficiency due to overloading of resources. Incoming inventory load with high predictability reduces patients' length of stay by up to 0.45 days, which is enabled by the ability of a worker to plan in advance for a new work assignment by discharging a patient to make room for the incoming one. With highly predictable incoming patients and no congestion on the day before expected discharge, there is a shift toward discharging patients currently in the hospital one day earlier than expected. A hospital would benefit from adding or allocating additional resources to the inpatient hospital units, and counter-intuitively, targeting a lower occupancy level to increase productivity. To further improve productivity, the allocated inpatient hospital resources could include adding a nurse on the hospital floors who is solely responsible for discharges and admissions. Closed for comment; 0 Comments.

• 03 Oct 2012

Can We Bring Back the “Industrial Commons” for Manufacturing?

Summing Up: Does the US have the political will or educational ability to remake its manufacturing sector on the back of an 'industrial commons?' Professor Jim Heskett's readers are dubious.

• 07 Aug 2012

When Supply-Chain Disruptions Matter

Disruptions to a firm's operations and supply chain can be costly to the firm and its investors. Many companies have been subjected to such disruptions, and the impact on company value varies widely. Do disruption and firm characteristics systematically influence the impact? In this paper, the authors identify factors that cause some disruptions to be more damaging to firm value than others. Insight into this issue can help managers identify exposures and target risk-mitigation efforts. Such insights will also help investors determine whether a company is exposed to more damaging disruptions. Key concepts include: The type of disruption matters in identifying the magnitude of a disruption's impact on a firm's share price. Disruptions attributed to factors within the firm or its supply chain are far more damaging than disruptions attributed to external factors. A higher rate of improvement in operating performance aggravates the impact of internal disruptions but not external disruptions. Management should be prudent about decisions to streamline operations and to reduce buffers and excess capacity. Some efficiency improvements may be attractive during periods of relative operational stability, but firms with high rates of improvement in operational performance could face distressing reductions in market value if they subsequently experience an internal disruption. Closed for comment; 0 Comments.

• 16 Apr 2012

The Inner Workings of Corporate Headquarters

Analyzing the e-mails of some 30,000 workers, Professor Toby E. Stuart and colleague Adam M. Kleinbaum dissected the communication networks of HQ staffers at a large, multidivisional company to get a better understanding of what a corporate headquarters does, and why it does it. Closed for comment; 0 Comments.

RESEARCH REPORT

Reinventing with a digital core

Chapter 1: How to accelerate growth through change

5-MINUTE READ

July 16, 2024

• To thrive amidst change and capture the value of disruptive technologies like generative AI, companies need a digital core that is reinvention ready.
• To do this, companies must 1) Build an industry-leading digital core 2) Boost investments in innovation 3) Balance technical debt.
• According to our research, doing all 3 created a 60:40 effect: 60% higher revenue growth rate and 40% higher profitability.

The technology capability your business needs, now

How can companies grow through constant change? This question is on the minds of executives as they face unprecedented disruption in their business environment.

Technology is now ranked the #1 most disruptive force today (up from #6 from a few years ago)—largely due to the rapid rise of generative AI. The enormous power of gen AI to reinvent every facet of business is not lost on companies.

However, expectations for gen AI are exceeding their current digital core capabilities:

of executives believe that they can scale gen AI enterprise-wide in 6 to 12 months.

are “extremely confident” that they have the right data strategies and the core digital capabilities in place to effectively leverage gen AI.

This gen AI-fueled, strategic shift towards reinvention has created an enormous need for a digital core: one that amplifies machines, humans and the interaction between the two in new and significant ways. Our analysis of 1,500 companies across 10 countries and 19 industries reveals that a digital core that is “reinvention-ready” is non-negotiable today.

What is the digital core?

We define the digital core as the critical technology capability that can create and empower an organization’s unique reinvention ambitions.

It enables organizations to accelerate ahead of competition and achieve their ambitions—using the right mix of cloud infrastructure and practices for agility and innovation; data and AI for differentiation; applications and platforms to accelerate growth, next-gen experiences and optimized operations—with security by design at every level.

Not all digital cores, however, are built the same, and every company’s journey will be different.

Powering the potential of generative AI 

The digital core is what enables gen AI—or any future disruptive technology—to deliver its full potential.

In our analyses, companies with industry-leading digital cores (top 25 percentile of our Digital Core Index) are reinventing twice as many functions with gen AI and are expected to create twice as much value. Early gen AI adopters see more benefits in terms of scale and scope of their projects.

Whether gen AI or the next new thing, the digital core is the engine that enables your company to drive reinvention with transformative technology.

What does it take to become reinvention ready?

Our research has identified three tenets that companies must follow to achieve reinvention readiness with the digital core:

Build an industry-leading digital core, tailored specifically to your industry and company.

Boost strategic investments in innovation in by 6% or more each year, including to re-engineer systems for machine (AI) operations.

Balance technical debt liabilities with investments for the future, targeting 15% of budgets, using programmatic and autonomous methods.

Taken together, these steps will enable a company to rapidly adopt new technologies and benefit from first-mover and fast-follower advantages.  In fact, companies that did all three experienced what we call the “60:40 effect”.

The 60:40 effect

higher revenue growth rate (from 7.1% to 11.1% on average)

higher profitability (from 14.2 to 19.4 percentage points on average)

And as of today, only 3% of companies have cracked this code. Now is the time to act on these three tenets, to pull ahead of the pack.

Build an industry-leading digital core for your industry and company

Our research shows that companies need to achieve an "industry-leading" level of digital core capability—defined as the top 25th percentile in our Digital Core Index—to empower continuous reinvention.

The Digital Core Index represents the aggregate strength of a company’s digital core as the average of each of its seven components' capabilities – their digital platforms, data, cloud-first infrastructure, composable integration, AI, security, and continuum control plane.

Digital Core Index

According to our research, achieving this “industry-leading” level, on its own, has many benefits:

Benefits of an industry-leading digital core

higher revenue growth rate

increase in profitability

strongly agreed their enterprise systems help them diversify into other geographies and industries

greater integration and end-to-end engineering and operations visibility (i.e., CCP)

Also promising—we found strong correlations between different components of the digital core. Which means when one is improved, it can create a ripple effect across others. For example, higher digital core capabilities enable greater AI adoption, while greater AI adoption can in turn further the development of the digital core. It’s a virtuous cycle.

Boost investments in innovation, including re-engineering systems for machine (AI) operations

Getting to a reinvention-ready digital core requires continuously boosting the proportion of IT budgets dedicated to strategic innovation (like gen AI). We found that shifting at least 6% yearly from maintenance to innovation was a recipe for success.

How are companies doing it? We see many reducing inefficiencies by rationalizing vendors, optimizing cloud costs and operationalizing wholesale automation. They can use those freed-up funds to redesign business processes, launch new products and services and enter new markets.

of companies are likely to grow their innovation budgets beyond 2023 at twice the intensity than they have before.

Designing with machines in mind will help generate those efficiency payoffs. Consider a future supply chain process where things like data collection, trend analysis, pre-ordering, system optimization are all designed to be accomplished end-to-end by machines. Humans can still intervene at any step, but the resulting system increases efficiencies and saves costs that can be reinvested.

Balance technical debt liabilities with future investments using programmatic methods

Many companies that relied on a “move-as-fast-as-you-can” strategy during the pandemic found themselves with mounting technical debt. Today, a more balanced approach to innovation is needed.

Our analysis reveals that leading companies allocate, on average, 15% of the IT budget toward tech debt remediation. It’s the sweet spot that enables “paying down debt” without sacrificing strategic investments.

Today’s companies are using gen AI and other technologies to maintain evergreen IT. But our research revealed that AI has risen to become a leading contributor to tech debt, tied with applications.

Clearly AI can be a double-edged sword. This means that companies looking to rapidly scale gen AI capabilities must invest in tech debt remediation to counter the potential pitfalls.

We see more programmatic approaches being used to contain technical debt at code, infrastructure and other parts of the system.

Benefits of reinvention readiness

Creating a reinvention-ready digital core simplifies the adoption of new technologies like gen AI. It also enables companies to break other performance barriers.

Companies adhering to all three tenets are:

more likely to say that their digital core enabled them to identify and mitigate risks (cyber, regulatory, Responsible AI) across multiple technologies, applications and ecosystem partners.

more likely to say that their digital core enabled their non-IT employees to create their own customized solutions using low code/no code tools.

New ways of working

To be reinvention-ready, a company must also continuously adopt new ways of working, including new operating models, methods and processes for their workforce. This starts at the team level, and many companies are making great strides:

report building strong capabilities around dynamic teams, where team members can rotate on and off based on project needs

are developing strong multi-disciplinary teams that are cross-functional and integrate technology and other skills

of companies in the top quartile of our Digital Core Index design talent capabilities and technology solutions to enable continuous change (vs. 49% of others)

So where do you start?

In this chapter, we established that a digital core is critical for a company to carry out continuous reinvention.

Our next chapter will share how to build your digital core strength. We’ll illustrate the new engineering principles you should apply on your journey, and provide a roadmap to reinventing with a digital core.

Karthik Narain

Group Chief Executive – Technology

Paul Daugherty

Chief Technology & Innovation Officer

Koenraad Schelfaut

Lead – Technology Strategy & Advisory

Global Technology Consulting Lead

Prashant P. Shukla, PhD

Principal Director – Accenture Research

Global Managing Director – Thought Leadership & Technology Research

New technique could help treat aggressive brain tumors

• Sarah Boudreau

16 Jul 2024

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Tackling brain cancer is complicated, but groundbreaking new research could help add another tool to the cancer-fighting arsenal.

A team from Georgia Tech and Virginia Tech published a paper in APL Bioengineering in May that explores a new option that could one day be used to target glioblastoma, a deadly and fast-growing brain tumor.  

Supported by National Institutes of Health grants, this work stems from past research on high frequency irreversible electroporation, better known as H-FIRE. H-FIRE is a minimally invasive process that uses non-thermal electrical pulses to break down cancer cells.

Treating any type of cancer isn’t easy, but when it comes to brain cancers, the blood-brain barrier adds an extra challenge. The barrier defends the brain against toxic material — but that’s not always a positive thing.

"Mother Nature designed it to prevent us from poisoning ourselves, but unfortunately, the way that works, it also excludes about 99 percent of all small-molecule drugs from entering the brain and achieving adequate concentrations to elucidate their therapeutic effect. That's particularly true for chemotherapeutics, biologics, or immunotherapies,” said  John Rossmeisl , the Dr. and Mrs. Dorsey Taylor Mahin Professor of Neurology and Neurosurgery at the Virginia-Maryland College of Veterinary Medicine . Rossmeisl is one of the paper’s coauthors. 

The square-shaped wave typically used with H-FIRE performs double duty: It disrupts the blood-brain barrier around the tumor site while destroying cancer cells. However, this was the first study to use a sinusoidal wave to disrupt the barrier. This new modality is called burst sine wave electroporation (B-SWE).

The researchers used a rodent model to study the effects of the sinusoidal wave versus the more conventional, square-shaped wave. They found that B-SWE resulted in less damage to cells and tissue but more disruption of the blood-brain barrier. 

In some clinical cases, both ablation and blood-brain barrier disruption would be ideal, but in others, blood-brain barrier disruption may be more important than destroying cells. For example, if a neurosurgeon removed the visible tumor mass, the sinusoidal waveform could potentially be used to disrupt the blood-brain barrier around the site, allowing drugs to enter the brain and eliminate the last of the cancer cells. B-SWE could result in minimal damage to the healthy brain tissue. 

Research indicates that the conventional square waveforms show good blood-brain barrier disruption, but this study finds even better blood-brain barrier disruption with B-SWE. This could allow more cancer-fighting drugs to access the brain.

"We thought we had that problem solved, but this shows you that with some forward thinking, there's always potentially better solutions,” said Rossmeisl, who also serves as associate head of the Department of Small Animal Clinical Sciences.

During the study, the researchers hit a snag: In addition to more blood-brain barrier disruption, they found that the sinusoidal wave also caused more neuromuscular contractions. These muscle contractions run the risk of damaging the organ. However, by tweaking the dose of B-SWE, they were able to reduce the contractions while providing a level of blood-brain barrier disruption similar to that of a higher dose.

The next step in this research is to study the effects of B-SWE using an animal model of brain cancer to see how the sinusoidal waveform stands up against the conventional H-FIRE technique.

The project was spearheaded by first author Sabrina Campelo while she completed her Ph.D. at the Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences. Campelo is now a postdoctoral fellow at the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University.

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• UB ranked among nation’s best for impact of its global health research, climate action and clean energy pursuits

UB ranked among nation’s best for impact of its global health research, climate action and clean energy pursuits

Solar arrays like this one on UB's North Campus have helped UB rank among the best universities in the nation in Times Higher Education's Impact Rankings. Photo: Douglas Levere

By David J. Hill

Release Date: July 15, 2024

BUFFALO, N.Y. –  In a measure of the positive impact of its global health research, climate action activities and commitment to clean energy, the University at Buffalo is among the best in the nation according to the Times Higher Education Impact Rankings.

Now in their sixth year, THE’s Impact Rankings identify and highlight universities from around the globe that excel across multiple United Nations Sustainable Development Goals (SDGs) . They are the only global performance rankings that assess universities against the SDGs.  UB has performed very well in the rankings since they were first introduced in 2019. 

This year, Times Higher Education evaluated 2,152 universities — 447 more than last year — from 125 countries/regions.

UB ranks among the top 10 among U.S. colleges and universities in four of the 17 SDG rankings, including No. 1 in Good Health and Well-Being, and No. 2 in Climate Action.

“We are immensely proud of the UB faculty, students and staff whose research and innovations positively impact the world and whose dedication is helping our university achieve its sustainability goals,” said A. Scott Weber, UB’s provost and executive vice president for academic affairs. “These rankings are a recognition of UB’s collective efforts across departments and academic disciplines.”

Added Laura Hubbard, UB’s vice president for finance and administration: “We are proud of the efforts of our campus community in integrating sustainability into our operations and practices across the university. Advancing the Sustainable Development Goals and the purpose they represent not only aligns with our values but also represents a smart and long-term strategic investment in UB and our broader community.”

Here’s a breakdown of UB’s Impact Rankings:

No. 1 (U.S.): Good Health and Well-Being

This ranking recognizes UB’s impactful research on the key conditions and diseases that have a disproportionate impact on health outcomes across the world, as well as UB’s support for health care professions and efforts to support the health of students, faculty and staff. 

In this category, UB is ranked No. 1 among U.S. universities and No. 38 globally among 1,498 universities submitting data.

This ranking also recognizes impactful public health outreach programming, such as the spring break trip to Belize that students from the Jacobs School of Medicine and Biomedical Sciences at UB took to train 100 farmers and villagers in lifesaving “Stop the Bleed” techniques. There are also the mobile dental clinics operated by the School of Dental Medicine, which provide dental care in rural and urban communities in Western New York.

UB’s ranking in this category also recognizes the significant number of students who graduate from UB with a degree associated with a health-related profession.

No. 2 (U.S.): Climate Action

UB’s ranking in this category is due in large part to UB’s “10 in 10” climate action plan of innovative, engaging and practical steps the university is taking to achieve net zero emissions by 2030.

Globally, UB is ranked 18th in this category among 924 universities worldwide, including an additional 189 institutions evaluated this year.

This ranking also recognizes UB’s impactful research on climate change , which includes $3 million in funding from the National Science Foundation to study the impacts of prehistoric climate change on ecosystems in Alaska ; a report on how climate change will affect New York State’s agriculture, buildings, ecosystems and economy; and studying Greenland’s glacier retreat rate over the past two decades.

No. 3 (U.S.): Affordable and Clean Energy

This ranking recognizes UB’s research related to energy, its energy use and policies, and the university’s commitment to promoting energy efficiency in the wider community.

UB has excelled in this area in recent years by advancing its commitment to clean energy through a variety of sources, including the recent installation of five solar arrays on university-owned land on the North Campus and four rooftop solar installations, making UB one of the largest on-campus producers of renewable energy in the country.

Top 5 (U.S.): Industry, Innovation and Infrastructure

This ranking recognizes UB’s role in fostering innovation and serving the needs of industry. It’s based on institutions’ research in support of industry and innovation, and the number of patents and spin-off companies a university produces.

UB’s Office of Business and Entrepreneur Partnerships offers several programs supporting the spin-off of faculty research and the start-up and growth of new companies.

UB is also playing a leadership role in state and federal initiatives to support research and development. For example, to accelerate AI research and innovation in New York State, Gov. Kathy Hochul named UB the home of Empire AI .

And this month UB and partners in the Buffalo-Rochester-Syracuse region were  awarded $40 million in federal funding to help boost the nation’s semiconductor manufacturing, research and education.

No. 9 (U.S.): Sustainable Cities and Communities

UB’s ranking in this category recognizes the university’s sustainable practices. This includes a new, 10-year contract for campuswide bus service that aims to have 40% of the fleet comprised of electric vehicles over the first two years of the contract, as well as the UB Regional Institute’s One Region Forward Climate Action Plan .

To learn more about the Sustainable Development Goals, visit UB’s guide of academic resources for each SDG.

Media Contact Information

David J. Hill Director of Media Relations Public Health, Architecture, Urban and Regional Planning, Sustainability Tel: 716-645-4651 [email protected]

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Lead channel, dtcc survey identifies improvements in industry preparedness for expanded us treasury clearing.

Source: DTCC

The Depository Trust & Clearing Corporation (DTCC), the premier post-trade market infrastructure for the global financial services industry, today issued a white paper, “The U.S. Treasury Clearing Mandate: An Industry Pulse Check,” to further enhance the industry’s knowledge of the U.S. Securities & Exchange Commission’s (SEC) expanded clearing rules.

The paper, which reports on findings from a recent industry survey conducted by DTCC’s Fixed Income Clearing Corporation (FICC) subsidiary, provides updated estimates on the volume of transactions required to be submitted for central clearing, the planned usage of FICC’s various access models, and how margin and liquidity risk management resources may be impacted.

“FICC stands at the center of a momentous transformation of market structure,” said Brian Steele, DTCC Managing Director, President, Clearing & Securities Services. “FICC continues to regularly survey the industry in order to provide new estimates on the impact of the rule. This most recent survey demonstrates that the industry’s level of understanding and preparedness for expanded clearing has significantly improved since we conducted our survey in 2023, as evidenced by further refinements to volume, liquidity and margin impacts.”

Specifically, new findings confirm:

• Volumes: Treasury clearing activity is expected to increase by more than $4TN each day, bringing the total activity to over $11.5TN in daily volume. 58% of respondents forecasted that the additional clearing activity will be driven by indirect participant Repo activity clearing.

• Access Models: FICC’s Sponsored Service continues to be the preferred access model for indirect participant activity (74% of respondents), however, survey respondents showed increased interest in the Agent Clearing Member (ACM) Service (43%). FICC expects to set up 7,000+ new intermediary-indirect participant relationships. • Done-Away Clearing: FICC anticipates growing adoption of a done-away clearing model for indirect participants. 28% of respondents say they expect to facilitate Treasury clearing activity through a business area that offers done-away execution.

• Margin: Margin is expected to increase but is anticipated to be proportionate to the additional volume of activity. Treasury Repo and Buy/Sell activity estimates indicate an increase in aggregate margin (VaR) for the respondents’ portfolios of approximately $58.4BN, with approximately $27BN (or 46%) of the aggregate incremental VaR representing segregated indirect participant margin. The results underscore FICC’s current efforts to provide margin efficiency through expanded end-user customer cross-margining opportunities.

• Liquidity: Survey results indicate a potential maximum daily liquidity need of US$84.5B, which would point to a total Capped Contingency Liquidity Facility (CCLF) size of US$109.9B under current parameters and the outlined assumptions. Since 2021, the CCLF facility has ranged between $71.0BN and $128.4BN. FICC will leverage these responses in its efforts to further diversify additional, cost-effective liquidity resources.

“Providing updated and transparent information is critical in supporting firms as they get ready for expanded central clearing of Treasuries,” said Laura Klimpel, Managing Director, Head of DTCC’s Fixed Income and Financing Solutions. “We remain committed to guiding and informing our members and the broader industry about the impact of the SEC’s new requirements, to collaborating with market participants on continuously enhancing our offerings, and to supporting a smooth and orderly implementation.” 

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