Effects of financial agglomeration on green total factor productivity in Chinese cities: Insights from an empirical spatial Durbin model

Xie, Rui; Fu, Wei; Yao, Siling; Qi, Zhang

doi:10.1016/j.eneco.2021.105449

Cited by 159 publications

(44 citation statements)

References 41 publications

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“…It means that CETS may have an impact on the spatial distribution of financial capital, which, in turn, will affect the spatial allocation efficiency of financial elements and change the city's GTFP. We use the location entropy of the financial industry to measure the level of financial agglomeration at the city level [51]. We apply the following model to test the financial agglomeration effect:…”

Section: Financial Agglomeration Effectmentioning

confidence: 99%

Can the Carbon Emissions Trading System Improve the Green Total Factor Productivity of the Pilot Cities?—A Spatial Difference-in-Differences Econometric Analysis in China

Huang

Chen

2022

IJERPH

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The carbon emission trading system (CETS) is an important market-oriented policy tool for the Chinese government to solve the problem of high emissions and achieve the growth of green total factor productivity (GTFP). This study makes up for the neglect of the spatial effect of CETS policy in previous studies and adopts the spatial difference-in-differences (DID) Durbin model (SDID-SDM) method of two-way fixed effects to scientifically identify the direct and spatial effects influencing the mechanisms and heterogeneity of CETS on urban GTFP based on the panel data of 281 cities in China from 2004 to 2017. It found that China’s CETS significantly improved the GTFP of pilot cities but produced a negative spatial siphon effect that restricted the growth of GTFP in surrounding cities. Benchmark results are robust under the placebo test, the propensity score matching SDID (PSM-SDID) test, and the difference-in difference-in-differences (DDD) test. The mechanism analysis shows that the CETS effect is mainly realized by improving energy efficiency, promoting low-carbon innovation, adjusting the industrial structure, and enhancing financial agglomeration. In addition, we find that policy effects are better in cities with high marketization, strong monitoring reporting and verification (MRV) capabilities, high coal endowment, and high financial endowment. Overall, China’s CETS policy achieves the goal of enhancing GTFP but needs to pay attention to the spatial siphon effect. In addition, our estimation strategy can serve as a scientific reference for similar studies in other developing countries.

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Section: Financial Agglomeration Effectmentioning

confidence: 99%

Can the Carbon Emissions Trading System Improve the Green Total Factor Productivity of the Pilot Cities?—A Spatial Difference-in-Differences Econometric Analysis in China

Huang

Chen

2022

IJERPH

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“…Second, the spatial spillover effect reported for each province incorporates spillover from all other provinces. Large spillover effects are commonly noted in the literature (e.g., Amin et al., 2019; Santos & Almeida, 2018; Xie et al., 2021). In the case of Model 1 in Table 2, where the spatial spillover effect is not considered, the direct impact on the rural household income is significantly overstated.…”

Section: Empirical Results and Discussionmentioning

confidence: 98%

“…This variable is expected to be related to R&D expenses but unrelated to the rural household income, and it passes the tests for under-identification, weak identification, and over-identification as reported in Appendix C. Furthermore, since we examine the spatial agricultural similarity matrix at the provincial level, effects of exogenous factors such as climate and weather have also been incorporated, which further reduces endogeneity. As a result, although R&D can be endogenous, most researchers treat WR&D as strictly exogenous (e.g., Alston et al, 2011;Amin et al, 2019;Andersen & Song, 2013;Jaffe, 1986;Santos & Almeida, 2018;Xie et al, 2021). Therefore, we follow the literature and also treat WR&D as exogenous.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

Enhancing rural income through public agricultural R&D: Spatial spillover and infrastructure thresholds

Zhan

et al. 2022

Review Development Economics

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How much farmers benefit from public agricultural research and development (R&D) has been a topic of continued interest. Compared to studies based on classical statistics, we show theoretically and employ the Spatial Durbin Model and the Panel Threshold Regression Model to investigate the different impacts and spatial spillover effects of public agricultural R&D on farm and nonfarm income. Using China's provincial panel data from 2000 to 2015, we find that public agricultural R&D has a positive impact on rural household income. This impact is mainly reflected by nonfarm income and exhibits heterogeneous interprovincial-spatial spillover.We also find that interregional differences in infrastructure, represented by road transportation and Internet access in rural areas, are the main reasons for the spatial heterogeneity. Higher road density and Internet penetration enhance the positive impact of public agricultural R&D on rural income, and the enhancement exhibits characteristics of thresholds, after which the size of enhancement becomes increasingly larger.

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“…In consideration of the spatial autocorrelation characteristics of EWP, this article employs the spatial econometric model to assess the effect of green credit on EWP (Zhao P. et al, 2022). Before conducting the empirical analysis, this study firstly conducts LM test, LR test, and Hausman test based on panel data (Xie et al, 2021). The results are shown in Table 1.…”

Section: Spatial Econometric Modelmentioning

confidence: 99%

Research on the effect of green credit on ecological welfare performance: Evidence from China

Zhang

Cao

et al. 2022

Front. Environ. Sci.

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Achieving high-quality economic development is a crucial feature of China’s new development stage. As an environmental regulation tool by implementing a differentiated credit policy, green credit is an inevitable choice to achieve high-quality economic development. In this paper, ecological welfare performance is used to measure the level of high-quality economic development. And based on the panel data of 30 provinces during 2008–2019, the spatial Durbin model and mediating effect model are used to empirically study the direct and indirect effect of green credit on ecological welfare performance in China. The results show that: (1) China’s ecological welfare performance shows the positive global spatial correlation in the overall spatial scope, with local characteristics of high–high aggregation and low–low aggregation. (2) Green credit can significantly promote the ecological welfare performance of local regions, but has a negative spatial spillover effect on the ecological welfare performance of adjacent regions. (3) Green credit can boost ecological welfare performance by improving technological innovation, industrial structure upgrading, and energy consumption structure. Based on these conclusions, the policy recommendations are put forward.

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Effects of financial agglomeration on green total factor productivity in Chinese cities: Insights from an empirical spatial Durbin model

Cited by 159 publications

References 41 publications

Can the Carbon Emissions Trading System Improve the Green Total Factor Productivity of the Pilot Cities?—A Spatial Difference-in-Differences Econometric Analysis in China

Can the Carbon Emissions Trading System Improve the Green Total Factor Productivity of the Pilot Cities?—A Spatial Difference-in-Differences Econometric Analysis in China

Enhancing rural income through public agricultural R&D: Spatial spillover and infrastructure thresholds

Research on the effect of green credit on ecological welfare performance: Evidence from China

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