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Jiang Y, Ramzan M, Awosusi AA, Adebayo TS. Moderating role of green innovation and fiscal expenditure towards achieving the Sustainable Development Agenda 2030 at provincial-level in China: policy implication from green total factor productivity. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:102818-102838. [PMID: 37674063 DOI: 10.1007/s11356-023-29551-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 08/23/2023] [Indexed: 09/08/2023]
Abstract
Amidst resource loss and environmental protection constraints, achieving green development necessitates enhancing green total factor productivity (GTFP) as a means of promoting rational and efficient resource allocation, thereby balancing economic growth and environmental preservation. Meanwhile, literature on the subject matter of GTFP from a sustainability viewpoint is minimal. As a result, this study employs the panel dataset from 30 provinces of China spanning the period 2005 to 2020 and utilizes the method of moments quantile regression (MMQR) developed by Machado and Santos Silva (2019) to analyze the heterogeneous role of green innovation, environmental regulations, and fiscal expenditure on GTFP. Moreover, the controlling variable for this study includes renewable energy and economic growth. Furthermore, this study investigates the heterogeneous combined impact of green innovation and fiscal expenditure (GTE*FSE) on GTFP. The findings of the MMQR reveal that green innovation has a positive impact on GTFP, while fiscal expenditure, environmental regulations, and renewable energy consumption have a negative impact. GTE*FSE has a positive and significant effect on GTFP, indicating that FSE can reinforce and increase the positive impact of GTE on GTFP in the long run. The study also reveals that economic growth has a mixed effect on GTFP, depending on the quantiles. Furthermore, environmental regulation has a significant and negative impact on GTFP, contradicting the Porter hypothesis. Likewise, the robustness of the findings is confirmed by the results of the fully modified OLS (FMOLS) and dynamic OLS (DOLS) estimations, which indicate a similar impact of the determinants on GTFP as observed in the MMQR analysis. This reinforces the validity of the findings and suggests that the observed relationships are robust to different estimation techniques. Furthermore, the findings of the Dumitrescu and Hurlin (D-H) panel causality test reveal significant bidirectional causality between renewable energy consumption and GTFP and fiscal expenditure and GTFP. Policy-makers need to channel a large chuck of their fiscal spending into green innovation so as to boost sustainability.
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Affiliation(s)
- Yongzhong Jiang
- College of Management Science, Chengdu University of Technology, Chengdu, 610051, China
| | - Muhammad Ramzan
- Faculty of Management and Administrative Sciences, Department of Business Administration, University of Sialkot, Punjab, Pakistan.
- Adnan Kassar School of Business, Lebanese American University, Beirut, Lebanon.
| | - Abraham Ayobamiji Awosusi
- Department of Economics & Data Sciences, New Uzbekistan University, Tashkent, Uzbekistan
- Faculty of Economics, Administrative and Social Science, Department of Economics, Bahçeşehir Cyprus University, Northern Cyprus, Mersin 10, Turkey
| | - Tomiwa Sunday Adebayo
- Faculty of Economics and Administrative Science, Department of Business Administration, Cyprus International University, Northern Cyprus, Mersin 10, Turkey
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Ding R, Zhou T, Yin J, Zhang Y, Shen S, Fu J, Du L, Du Y, Chen S. Does the Urban Agglomeration Policy Reduce Energy Intensity? Evidence from China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14764. [PMID: 36429482 PMCID: PMC9690510 DOI: 10.3390/ijerph192214764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 11/07/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
With the expansion of the scale of China's economy and the acceleration of urbanization, energy consumption is increasing, and environmental degradation and other problems have arisen. In order to solve such prominent problems, China proposed the "carbon peak" and "carbon neutral" targets in 2020. Although there are research conclusions about the impact of urbanization on energy intensity (EI), conclusions about the impact of the urban agglomeration policy (UAP) on EI are still unclear. Therefore, the article studies the impact of the urban agglomeration policy on EI in 279 prefecture-level cities by constructing a Difference-In-Differences (DID) model and mediating effect model. The results show that UAP has a significant effect on reducing EI, but their effects are different with the impact of urban heterogeneity, and the urban agglomeration policy of "Core" cities is less effective than those of "Edge" cities. From the perspective of the influencing mechanism, UAP takes green innovation capability as the intermediary variable to influence EI. The placebo test, PSM-DID regression, counterfactual test, and instrumental variable method all reflect the robustness of the research conclusions. Based on this, the paper puts forward some suggestions for urban agglomeration planning and green technology innovation.
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Affiliation(s)
- Rui Ding
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
- Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
- Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Tao Zhou
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
- Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
- Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Jian Yin
- West China Modernization Research Center, Guizhou University of Finance and Economics, Guiyang 550025, China
- School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin 150050, China
| | - Yilin Zhang
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
- Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
- Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Siwei Shen
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
- Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
- Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Jun Fu
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
- Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
- Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Linyu Du
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
- Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
- Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Yiming Du
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
- Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
- Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
| | - Shihui Chen
- College of Big Data Application and Economics (Guiyang College of Big Data Finance), Guizhou University of Finance and Economics, Guiyang 550025, China
- Guizhou Key Laboratory of Big Data Statistical Analysis, Guizhou University of Finance and Economics, Guiyang 550025, China
- Key Laboratory of Green Fintech, Guizhou University of Finance and Economics, Guiyang 550025, China
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