On green credits and carbon productivity in China

Time-varying impacts of green credit on carbon productivity in China: New evidence from a non-parametric panel data model

In the context of global climate change threatening human survival, and in a post-pandemic era that advocates for a global green and low-carbon economic recovery, conducting an in-depth analysis to assess whether green finance can effectively support low-carbon economic development from a dynamic perspective is crucial. Unlike existing research, which focuses solely on the average effects of green credit (GC) on carbon productivity (CP), we introduce a non-parametric panel data model to investigate GC's impact on CP across 30 provinces in China from 2003 to 2021, verifying a significant time-varying effect. Specifically, during the first phase (2003-2008), GC negatively impacted CP. In the second phase (2009-2014), this negative influence gradually diminished and transformed into a positive effect. In the third phase (2015-2021), GC continued to positively influence CP, although this effect became insignificant during the pandemic. Further subgroup analysis reveals that in the regions with low environmental regulations, GC did not significantly boost CP throughout the sample period. In contrast, in the regions with high environmental regulations, GC's positive effect persisted in the mid to late stages of the sample period. Additionally, compared to the regions with low levels of marketization, the impact of GC on CP was more pronounced in highly marketized regions. This indicates that the promoting effect of GC on CP depends on strong support from environmental regulations and well-functioning market mechanisms. By adopting a non-parametric approach, this study reveals variations in the impact of GC on CP across different stages and under the influence of the pandemic shock, offering new insights into the relationship between GC and China's CP.

Green credit and low-carbon development in China: Fresh evidence on spatial spillover insights

The promulgation and execution of green credit (GC) policies have had a significant influence on the development of the economy and society, and their impact on low-carbon development (LCD) needs to be taken seriously. On the basis of elaborating the mechanism of the role of GC on LCD, this study constructs a panel fixed effects (FE) model to test the direct impact of GC on LCD by using Chinese provincial-level data from 2007 to 2020. An intermediary effect model is constructed to investigate its indirect effects. A dynamic SDM is further constructed to examine the spatial effects of GC on LCD in neighbouring regions. The results show that GC is helping China to achieve LCD. GC can promote LCD through promoting green innovation, optimizing energy structure and upgrading industrial structure. It is crucial to acknowledge that all three pathways are essential channels of influence and should not be disregarded. GC not only fosters LCD in the local areas, but also has a positive spatial spillover effect in adjacent regions. Based on the above conclusions, this study proposes policy recommendations such as increasing support for GC, smoothing the transmission channel from GC to LCD, and establishing a synergistic linkage mechanism between interregional credit and environmental governance. This study provides valuable insights for China to realize LCD, as well as for other countries to actively engage in energy conservation and emission reduction efforts.

Spatial effects of trade, foreign direct investment (FDI), and natural resource rents on carbon productivity in the GCC region

Background

Natural resource rents (NRRs) may determine the environment and economic growth of the GCC countries due to their over-reliance on the natural resource sector. NRRs are the source of income in resource-abundant GCC countries. So, increasing income of these countries could pollute the environment by increasing overall economic activities. Consequently, NRRs could determine carbon productivity in the GCC region through increasing income and carbon emissions.

Methods

The effects of trade openness (TO), foreign direct investment (FDI), urbanization, and oil and natural gas rents on carbon productivity (CP) are examined in the GCC region from 1980-2021 using the spatial Durbin model.

Results

The CP of the GCC countries has spillovers in their neighboring countries. Oil rent reduces carbon productivity in domestic economies and the entire GCC region. Natural gas rent, TO, and FDI increase, and urbanization reduces carbon productivity in neighboring economies and the entire GCC region. Moreover, urbanization reduces carbon productivity in domestic economies as well. The study recommends the GCC countries to reduce reliance on oil rent and increase globalization in terms of TO and FDI in the region to promote carbon productivity. Moreover, GCC countries should also focus more on natural gas rent instead of oil rent to raise carbon productivity.

Incentive or constraint? Comprehensive impacts of green credit policy on industrial energy intensity

Green credit policy (GCP) has dual attributes of being both an "environmental regulation" and a "financial instrument". Understanding its role in facilitating industrial green transformation is crucial. However, there is limited theoretical and empirical evidence on the impact of GCP on industrial green transformation. This research fills this gap by comprehensively investigating the impacts and mechanisms of GCP on industrial energy intensity (EI) in China, considering both incentive and constraint effects. Theoretically, the environmental and financial impacts of GCP are merged into a unified analytical framework based on a heterogeneous enterprise model. Empirically, diverse empirical methods, including difference-in-differences (DID), difference-in-differences-in-differences (DDD), and mediating effects models, are adopted to examine whether GCP can promote green innovation or accelerate financial constraints. Results show that (1) GCP significantly decreases EI, especially among high-polluting enterprises (HPEs). The impact of incentives is far greater than that of constraints. (2) Regarding the incentive effect, energy substitution and innovation offsets exert a primary influence on reducing EI. (3) The constraint effect is caused primarily by rising financing and pollution abatement costs. (4) Heterogeneity analysis shows that the inhibiting effect of GCP is more significant in non-state-owned enterprises, underdeveloped financial markets, and abundant energy endowments. This paper provides a theoretical framework and new empirical evidence for policymakers to design effective policies for promoting industrial green transformation.

How does green credit reduce carbon emissions? Dynamic spatial interactions and regional disparities

Green credit is an important green financial policy tool to promote green development. However, research is needed to explore how green credit reduces carbon emissions, especially with respect to its dynamic spatial interactions and regional disparities. Based on a theoretical analysis, this paper empirically tests the carbon emission reduction effect of green credit and its three mechanisms by combining a Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, dynamic spatial Durbin model (SDM), and the mediation model, including their dynamic spatial interactions and regional disparities. The study concludes that green credit can reduce carbon emission intensity based on strong spatio-temporal interactions in China. Green credit mainly reduces carbon emission intensity through scale and technology mechanisms with different spatio-temporal interactions. The tertiary industry in China does not currently have completely clean production; as such, the upgrading of the industrial structure as stimulated by green credit in the long term cannot yet effectively reduce carbon emissions. In addition, the carbon emission reduction effect of green credit and its three mechanisms have different levels of performance and dynamic spatial interactions in different regions of China. Finally, targeted policy recommendations are proposed to apply green credit to effectively reduce the carbon emission intensity.

The impact of green credits on high-quality energy development: evidence from China

The implementation of green credits has become an important engine for China's high-quality energy development (HQED). On the basis of constructing an index of HQED and the panel data of thirty provinces in China from 2008 to 2019, this study empirically investigated the effects of green credits on HQED and the action mechanisms behind it in a multi-dimensional manner using a panel fixed-effects model, mediating-effects model, and spatial Durbin model. The results indicated that green credits had significantly contributed to China's HQED, and that conclusion still held true after a series of robustness tests were conducted. It was found that industrial structures and human capital were important channels through which green credits influenced China's HQED. Moreover, the spatial spillover effects of green credits on HQED were also confirmed. Finally, in terms of temporal heterogeneity, the positive effects of green credits on HQED were found to have increased significantly after 2012. Also, in terms of regional heterogeneity, this study observed that the positive influence of green credits on HQED was more significantly in central and western China than in eastern China, and in southern China than in northern China. The results obtained in this research investigation will potentially provide some important insights for energy planners and policymakers to further the understanding of the drivers of HQED, and the corresponding transmission mechanisms and regional differences.

Green credit and industrial green total factor productivity: The impact mechanism and threshold effect tests

Green credit is an important financial policy tool to solve environmental pollution problems. Improving industrial green total factor productivity (IGTFP) is the key to promote industrial green development. Our study adopts provincial data from 2005 to 2020 to investigate the influence of green credit (GC) on IGTFP. We find that GC significantly improves IGTFP on the whole, industrial structure upgrading and green innovation are the two key impact paths. Threshold model tests show that with the increase of GC, human capital and R&D intensity, the promoting effects of GC on IGTFP are significantly enhanced. Heterogeneity tests indicate that the promoting effect of GC on IGTFP was further enhanced after 2016, GC significantly promotes IGTFP in eastern China, but it is not obvious in central and western China. Besides, the promoting effect of GC on IGTFP is significantly enhanced with the increase of IGTFP. Our research shows that the government should further optimize the green credit system and play the role of green credit in promoting green innovation and industrial structure upgrading.