Agglomeration and green technology innovation efficiency of industrial enterprises – based on spatial statistical analysis

• Autorzy: Wu Mingran , Huang Weidong

Identyfikatory

DOI

10.24425/aep.2022.140761

• Języki publikacji: EN

Abstrakty

EN

Based on China’s provincial panel data from 2009 to 2019, this paper empirically tests and analyzes the effects of industrial agglomeration and other important economic variables on industrial green technology innovation efficiency from the perspective of spatial statistical analysis. The results show that the efficiency of China’s industrial green innovation has not changed much during the study period, exhibiting an obvious polarization phenomenon. Moreover, the improvement of the degree of industrial agglomeration is conducive to the regional green innovation efficiency level. This means that industrial agglomeration produces effective environmental and innovation benefits. In addition, the influence coefficient of enterprise-scale is negative, indicating that for Chinese industrial enterprises, the enlargement of the production scale weakens the promotion effect of R&D activities. The influence coefficient of human capital is negative, mainly because the direct effect has a small and positive value, while the indirect effect (spillover effect) has a negative and large value, indicating that the spillover effect of human capital between regions in China is deficient.

Słowa kluczowe

EN

agglomeration of industry; green innovation efficiency; spatial economics

• Wydawca: Institute of Environmental Engineering, Polish Academy of Sciences
• Czasopismo: Archives of Environmental Protection
• Rocznik: 2022
• Tom: Vol. 48, no. 2
• Strony: 3--14
• Opis fizyczny: Bibliogr. 52 poz., rys., tab., wykr.

Twórcy

autor

Wu Mingran

• School of Management, Nanjing University of Posts and Telecommunications, Information Industry Convergence Innovation and Emergency Management Research Center, Nanjing, China

autor

Huang Weidong

• School of Management, Nanjing University of Posts and telecommunications, Information Industry Convergence Innovation and Emergency Management Research Centre, Nanjing, China

Bibliografia

• 1. Anselin, L. (1988) Spatial econometrics: Methods and models, Dordrecht : Kluwer Academic.
• 2. Burchart-Korol, D.. & Zawartka, P. (2019). Environmental life cycle assessment of septic tanks in urban wastewater system - a case study for Poland, Archives of Environmental Protection, 45, 4, pp. 68–77. DOI: DOI 10.24425/aep.2019.130243.
• 3. Charnes, A, Cooper, W.W. & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operation Research, 2 , pp. 429-444.
• 4. Chen, S.Y. & Golley, J. (2014). Green productivity growth in China’s industrial economy, Energy Economics, 44, 7, pp. 89-98. DOI: 10.1016/j.eneco.2014.04.002.
• 5. Copper, W.W., Li, S., Seiford, L.M., et al. (2001) Sensitivity and stability analysis in DEA: Some recent development, Journal of Productivity Analysis, 15, pp. 217-246.
• 6. Duranton, G. & Puga, D. (1999). Diversity and specialisation in cities: Why, where and when does it matter?, Urban Studies, 37, 3, 533–555.
• 7. Haken, H. (1971). From the Laser to Synergetics: A Scientific Biography of the Early Years, Springer.
• 8. Hirte, G. & Tscharaktsciew, S. (2013). The optimal subsidy on electric vehicles in German metropolitan areas：a spatial general equilibrium analysis, Energy economics, 40, pp. 515-528. DOI: 10.1016/j.eneco.2013.08.001.
• 9. Hu AG, Zhou SJ. (2014) Green development: Functional definition, mechanism analysis and development strategy, China population, resources and environment, 24, 1, pp.14-20.
• 10. Humphrey J, Schmitz H. (1996) The triple C approach to local industrial policy, World Development, 24, pp. 1859-1877.
• 11. J Adu, M Kumarasamy. (2020) Mathematical model development for non-point source in-stream pollutant transport, Archives of Environmental Protection, 46, 2, pp. 91–99. DOI: DOI 10.24425/aep.2020.133479.
• 12. Jeon C，Lee J，Shin J. (2015) Optimal subsidy estimation method using system dynamics and the real option model：photovoltaic technology case, Applied energy, 142, pp. 33-43. DOI: 10.1016/j.apenergy.2014.12.067.
• 13. Ji ZH, Yu W, Zhang P. (2020) Spatial agglomeration of high-tech industries, technological innovation and regional green development efficiency: Empirical evidence based on PVAR model, Macroeconomics, 9, pp. 92-102.
• 14. Jiang L. (2016) The choice of spatial econometric models reconsidered in empirical studies, Journal of Statistics and Information, 31, pp. 10-16.
• 15. Krugman P. (1991b) Geography and trade, MIT press.
• 16. Krugman P. (1992) A dynamic spatial model, National Bureau of Economic Research.
• 17. Krugman P. (1993) First nature，second nature，and metropolitan location, Journal of regional science, 33, pp. 129-144. DOI: 10.1111/j.1467-9787.1993.tb00217.x
• 18. Krugman P．(1991a) Increasing returns and economic geography, Journal of Political Economy, 99, pp. 483-499.
• 19. Kuznetsov A V, Kuznetsova O V. (2019) The success and failure of Russian SEZs: Some policy lessons, Transnational Corporations Journal, United Nations Conference on Trade and Development.
• 20. Li XL. (2014) An empirical analysis based on marketization, industrial agglomeration and environmental pollution, Statistical Research, 8, pp. 39-45. https://tjyj.stats.gov.cn/EN/Y2014/V31/I8/39.
• 21. Liu B. (2012) Research on the development strategy of agglomeration of producer service industry in Shanghai, East China Economic Management, 26, 1, pp. 1-3.
• 22. Liu J, Cao YR, Wu HT. (2020) The influence of industrial co-agglomeration on regional green innovation, Forum on Science and Technology in China, 4, pp. 42-50.
• 23. Liu, S., Zhu, Y. & Du, K. (2017) The impact of industrial agglomeration on industrial pollutant emission: evidence from China under New Normal, Clean Technologies and Environmental Policy, 19, pp. 2327-2334. DOI: 10.1007/s10098-017-1407-0
• 24. Luo LW, Liang SR. (2017) The spatial effect of international R&D capital technology spillovers on the efficiency of China's green technology innovation, Business Management Journal, 39, pp. 21-33.
• 25. M G Lukaszewska, Z Pawlak, G Sinicyn. (2021) Spatial distribution of the water exchange through river cross-section – measurements and the numerical model, Archives of Environmental Protection, 47,1, pp. 69–79. DOI 10.24425/aep.2021.136450
• 26. Marshall, A. (1920). Industry and trade: A study of industrial technique and business organization, London: Mac Millan
• 27. MJ Wiatkowski, B Wiatkowska, U Gruss, C Rosik-Dulewska, D Chopek (2021) Assessment of the possibility of implementing small retention reservoirs in terms of the need to increase water resources, Archives of Environmental Protection, 47,1, pp. 80–100. DOI 10.24425/aep.2021.136451
• 28. Newman C, Page J M. (2017) Industrial clusters: The case for special economic zones in Africa, Wider Working Paper Series wp-2017-15, World Institute for Development Economic Research (UNU-WIDER).
• 29. P M Bochenska, W Rzeznik. (2019) Ammonia emission from livestock production in Poland and its regional diversity, in the years 2005–2017, Archives of Environmental Protection, 45, 4, pp. 114–121. DOI 10.24425/aep.2019.130247
• 30. P Tomczyk, M Wiatkowski. (2020) Shaping changes in the ecological status of watercourses within barrages with hydropower schemes – literature review, Archives of Environmental Protection, 46, 4, pp. 78–94. DOI 10.24425/aep.2020.135767
• 31. P Wilk, A Grabarczyk. (2018) The effect of selected inviolable flow characteristics on the results of environmental analysis using the example of river absorption capacity, Archives of Environmental Protection, 44, 2, pp. 14–25. DOI 10.24425/119702
• 32. Pohl, A. & Kostecki, M.. (2020). Spatial distribution, ecological risk and sources of polycyclic aromatic hydrocarbons (PAHs) in water and bottom sediments of the anthropogenic lymnic ecosystems under conditions of diversified anthropopressure, Archives of Environmental Protection, 46 ,4, pp. 104–120. DOI 10.24425/aep.2020.135769
• 33. Porter M.E. (1998) Cluster and the new economics of competition, Harvard Business Review, 76, pp. 11-12.
• 34. Qu YF, Yu CQ. (2021) Diversification and specialization of industrial agglomeration and the efficiency of enterprise green technology innovation, Ecological Economy, 37, pp. 61-67.
• 35. Shen N, Peng H. (2021) Can industrial agglomeration achieve the emission-reduction effect? Socio-Economic Planning Sciences, 75, pp. 100867. DOI: 10.1016/j.seps.2020.100867.
• 36. Silvia C, Krause R M. (2016) Assessing the impact of policy interventions on the adoption of plug-in electric vehicles: an agent-based model, Energy policy, 96, pp.105-118. DOI: 10.1016/j.enpol.2016.05.039.
• 37. Storper M. (1992) The limits to globalization: technology districts and international trade, Economic Geography, 68, pp. 60-93.
• 38. Sun H X et al. (2019) Evolutionary game of the green investment in a two-echelon supply chain under a government subsidy mechanism, Journal of cleaner production, 235, pp.1315-1326. DOI: 10.1016/j.jclepro.2019.06.329.
• 39. Sun XH, Wang Y. (2014) The influence of firm size on productivity and its difference-Based on the empirical test of industrial firms in China, China Industrial Economics, 5, pp. 57-69.
• 40. Turkina E, Van Assche A. (2018) Global connectedness and local innovation in industrial clusters, Journal of International Business Studies, 49, pp. 706-728. DOI: 10.1057/s41267-018-0153-9.
• 41. Wang H, Miao Z, Wang SQ. (2015) Spatial spillover, industrial agglomeration effect and industrial green innovation efficiency, Forum on Science and Technology in China, 12, pp. 33-38.
• 42. Wang MK, Liu YP, Li T. (2019) The differential impact of tourism industrial agglomeration on environmental pollution: Empirical evidence from 287 cities, Reform, 2, pp. 102-114.
• 43. Wang XH, Feng YC. (2018) The influence of environmental regulation on China’s circular economy performance, 28, 7, pp. 136-146.
• 44. Wu MR, Ma J. (2016) Measurement on regional ecological efficiency in China and analysis on its influencing factors: Based on DEA-Tobit two-stage method, Journal of Technology Economics, 35, 3, pp. 75-80+122.
• 45. Wu MR, Zhao M and Wu ZD (2020) An evaluation and variation analysis of sustainable development capacity in different regions of China, International Journal of Environmental Technology and Management, 23, 5-6, pp. 397-413.
• 46. Wu MR, Zhao M. (2016) Research on Chinese different regional sustainable development capacity and its spatial differentiation, Shanghai Journal of Economics, 10, pp. 84-92.
• 47. Wu MR, Zhao M. (2017) The effect of linking and promotion of marketization on industrial agglomeration and industrial ecology efficiency: Based on an analysis to eastern China region, Journal of Nanjing Tech University (Social Science Edition), 16, pp. 115-123.
• 48. Wu MR. (2021) Measurement and spatial statistical analysis of green science and technology innovation efficiency among Chinese provinces, Environmental and Ecological Statistics, 28, pp. 423–444. DOI: 10.1007/s10651-021-00491-7
• 49. Xiao ZL, Du XY. (2017) Measurement and convergence in development performance of China’s high-tech industry, Science Technology and Society, 22, pp. 212-235. DOI: 10.1177/0971721817702280
• 50. Yang Z, Song T, Chahine. (2016) Spatial representations and policy implications of industrial co-agglomerations, a case study of Beijing, Habitat International, 55, pp. 32-45. DOI: 10.1016/j.habitatint.2016.02.007
• 51. Zhang K, Dou JM. (2016) Do Industrial Agglomeration Reduce Emissions? Journal of Huazhong University of Science and Technology (Social Science Edition), 30, 4, pp. 99-109.
• 52. Zhao HL, Lin BQ. (2019) Will agglomeration improve the energy efficiency in China’s textile industry: Evidence and policy implications, Applied Energy, 237, pp. 326-337. DOI: DOI: 10.1016/j.apenergy.2018.12.068

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).