Achieving synergy between carbon mitigation and pollution reduction: Does green finance matter?

The synergy between carbon mitigation and pollution reduction (CMPR) serves as the predominate catalyst for facilitating green transformation. Although the Green Finance Polit Policy (GFPP) shares a commitment to green development, its effectiveness in fostering the synergy of CMPR remains uncertain. Hence, we adopted the Difference-In-Difference (DID) model to investigate the effect of GFPP on the synergy between CMPR during 2009-2019. Initially, a composite synergy degree model is employed to assess the synergy between CMPR at the prefectural level. Our empirical findings demonstrate a notable contribution of GFPP in augmenting the synergy between CMPR. Robustness tests using Tobit model, PSM-DID, and placebo tests (temporal and regional) also confirm the stability of the findings. Furthermore, the study identifies three plausible mechanisms, including industry optimization, green innovation, and energy structure, which are likely to impact the aforementioned synergy. Empirical tests provide supportive evidence that substantiates the theoretical validity of these mechanisms, indicating that optimizing industry structure and enhancing the quality of green innovation can amplify the positive impact of green finance. Additionally, the impact of green finance policy in augmenting the synergy between CMPR exhibits greater prominence in central regions, non-resource-based cities, and non-key environmental protection cities. Our study also uncovers an additional finding that green finance has a detrimental effect on the quality of green innovation, thereby impeding the advancement of synergy between CMPR. In summary, our findings offer novel insights into promoting the synergistic relationship between CMPR by means of implementing green finance policy.

Future land use simulation model-based landscape ecological risk prediction under the localized shared socioeconomic pathways in the Xiangjiang River Basin, China.

Landscape ecological risk (LER) is an effective index to identify regional ecological risk and measure regional ecological security. The localized shared socioeconomic pathways (LSSPs) can provide multi-scenario parameters of social and economic development for LER research. The research of LER under LSSPs is of scientific significance and practical value in curbing the breeding and spread of LER risk areas. In this study, land-cover raster files from 2010 to 2020 were used as the foundational data. Future land use simulation (FLUS), regression, and Markov chain models were used to predict the land cover patterns under the five LSSP scenarios in the Xiangjiang River Basin (XJRB) in 2030. Thus, an evaluation model was established, and the LER of the watershed was evaluated. We found that the rate of land cover change (LCC) in the XJRB between 2010 and 2020 had a higher intensity (increasing at an average of 18.89% per decade) than that projected under the LSSPs for 2020-2030 (averaging an increase of 8.58% per decade). Among the growth rates of all land use types in the XJRB, that of urban land was the highest (33.3%). From 2010 to 2030, the LER in the XJRB was classified as lower risk (33.73%), lowest risk (33.11%), and moderate risk (24.13%) for each decade. Finally, the LER exhibited significant heterogeneity among different scenarios. Specifically, the percentages of regions characterized by the highest (9.77%) and higher LER (9.75%) were notably higher than those in the remaining scenarios. The higher-level risk area under the localized SSP1 demonstrated a clear spatial reduction compared to those of the other four scenarios. In addition, in order to facilitate the differential management and control of LER by relevant departments, risk zoning was carried out at the county level according to the prediction results of LER. And we got three types of risk management regions for the XJRB under the LSSPs.

Empty talk or real action? Exploring how local government's attention to the environment affects urban land use efficiency.

Improving urban land use efficiency (ULUE) has emerged as an important strategy in cities pursuing sustainable development. Since 2007, governments at different levels have enhanced the emphasis on environmental protection, as embodied in their annual work reports. Thus, we became interested in whether local government's attention to the environment translates into real action or remains an empty promise. Using Chinese prefecture-level panel data from 2007 to 2020, we found that local government's attention to the environment was positively associated with ULUE, indicating that there is not a complete decoupling effect between attention and action. In addition, the relationship between environmental attention and ULUE was negatively moderated by the land finance channel and positively moderated by the industrial optimization channel. This study also employs a threshold model to precisely determine the ideal scale for land market agreements to maximize the role of the government. Notably, the positive effect of local government's environmental attention on ULUE was negated when the proportion of negotiated land transfer surpassed 0.1874. The findings provide a benchmark for governments to improve sustainability through land resource management.

Whether the digital divide widens the income gap between China's regions?

Based on the panel data of 280 prefecture-level cities in China from 2014 to 2018, we construct the digital divide index from three aspects including "access divide", "use divide" and "efficiency divide", using Thiel index to measure income inequality and study the impact of the digital divide on income inequality between different regions. We apply the two-stage spatial DID combined IV model. In the first stage, we introduce the "Internet + government affairs" policy to construct the spatial DID model, so as to obtain the estimated value of the digital divide index. When the estimated value is substituted into the two-stage regression as an instrumental variable, it is found that every 1unit increase in the digital divide will widen the income divide by 0.134 units. This conclusion is still robust under the replacement of Theil index and change of weights. The results of heterogeneity analysis show that digital divide has a more significant impact on the income divide in eastern China in terms of regions. It is found after distinguishing different types of digital divide that the other two types of digital divides have the most significant impact than income divide, 0.034 units higher than that of the traditional. Finally, we introduce the mechanism variables from three levels of "access divide", "use divide" and "efficiency divide", thus verifying the effect of information asymmetry, effect of human capital differentiation and effect of delaying industrial upgrade. Then we put forward the following policy recommendations: in order to reduce the deterioration effect of digital divide on income distribution, it is necessary to improve infrastructure, enhance the degree of digitalization of human capital and optimize the industrial structure.