Labor market distortion and air pollution: An empirical analysis based on spatial effect modeling

The impact of air pollution on career changes among Chinese workers

The influence exerted by air pollution on interregional workforce migration has garnered considerable attention in ecological economics over time; however, relatively scant consideration has been given to its effects on occupational transition dynamics. This study presents an empirical examination of the influence of air pollution on job changes among the working population and seeks to understand the underlying causal mechanisms. By merging detailed micro-level survey data with regional Fine particulate matter (PM2.5) data from Chinese counties spanning the years 1997 to 2015, we have constructed an extensive database to support our analysis. The study revealed that air pollution has a significant negative impact on the likelihood of career switching. Mechanistic analysis indicates that changes in wage compensation, as well as declines in health status, serve as the primary pathways through which air pollution exerts its influence. To reduce the welfare loss caused by air pollution, it is crucial to prioritize the welfare benefits and health conditions of the labor force.

Exploring the fiscal context of electricity consumption in China: Does the Chinese-style fiscal decentralization matter?

In the context of carbon neutrality, reducing electricity consumption is an inevitable requirement for sustainable development in China. It is of practical significance to understand the role of fiscal decentralization (FD) in energy conservation and emission reduction of local governments. This paper uses panel data of 30 provinces from 2004 to 2020 to build panel fixed effect, mediating effect and moderating effect models, and empirically analyzes the impact of Chinese-style FD on electricity consumption. The results find that FD significantly increases electricity consumption, but there is obvious heterogeneity. Infrastructure construction, industrial development and scale economies are the influencing channels through which FD affects electricity consumption. At the same time, marketization development is conducive to alleviating the promoting effect of FD on electricity consumption, but inter-governmental investment competition may strengthen the influence of FD. Moreover, as electricity demand rises, the impact of FD will intensify. Therefore, the research results of this paper highlight the importance of green fiscal reform.

Methods for Quantifying Source-Specific Air Pollution Exposure to Serve Epidemiology, Risk Assessment, and Environmental Justice

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult-since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

Does the integration of digital and real economies promote urban green total factor productivity? Evidence from China

Enhancing urban green total factor productivity (UGTFP) is of great significance in driving the green transformation of cities. The integration of the digital economy and real economy (IDR) offers a novel approach to improving UGTFP. This paper constructs a theoretical analytical framework to examine the impact of IDR on UGTFP. Utilizing a range of econometric models and Chinese urban data, this paper empirically analyzes the influence, mechanism, and spatial spillover effect of IDR on UGTFP. The findings reveal that China's IDR is currently at a stage of low integration, exhibiting significant regional disparities, with a spatial distribution pattern declining from east to west. IDR can significantly enhance UGTFP, and the conclusion is supported by robustness tests. Heterogeneity analysis indicates that the positive effect of IDR on UGTFP is more pronounced in the eastern and western regions, as well as in areas with a large Internet scale. The mechanism test suggests that economic agglomeration can significantly amplify the promotional effect of IDR on UGTFP. IDR can boost UGTFP by elevating innovation levels and optimizing labor resource allocation. Further exploration reveals a significant positive spatial spillover effect of IDR on UGTFP. These insights not only offer a practical pathway for green transformation but also provide empirical evidence for policymakers to formulate scientific policies, enhance coordination, and improve data governance, thereby facilitating high-quality and green economic development.

Smart city and green innovation: Mechanisms and industrial emission reduction effect

Green innovation is essential for environmentally sustainable development. The construction of smart cities offers significant potential for developing green innovation through optimizing urban administration and improving the allocation of critical resources. Using Chinese city data from 2005 to 2019, this study adopts a causal identification framework based on the multi-temporal difference-in-difference method to explore the impact of smart city construction on green innovation and the mechanism and joint industrial emission reduction effect between them. A positive and significant relationship with a weak inverted U-shaped trend was found between smart city construction and green innovation. Besides the direct channel, labor factor allocation, venture capital attractiveness, and market accessibility are essential indirect channels between the two concepts. Furthermore, the effects of smart city construction on green innovation varied depending on the marketization level, administrative rank, population size, and geographic location of the city. In addition, the interaction of the two constructs negatively affected industrial emissions, which helped optimize the environment. These findings suggest that smart city construction offers a digital dividend for developing green innovation and creating an efficient, sustainable environment.

Revealing the hidden carbon flows in global industrial Sectors-Based on the perspective of linkage network structure

This paper interprets the implicit carbon flows in global industrial sectors from a network perspective. Using the SNA-IO integrated model, along with cross-border input-output data from Eora26 (2000-2020) and global energy balance data, the implicit carbon emissions of global industrial sectors and their evolution are analyzed. A carbon emission network structure from an industrial chain perspective is proposed. The results indicate that the carbon emissions responsibility of an industry is not only associated with its own energy consumption. It also involves the carbon emissions transfer resulting from the exchange of products and services between upstream and downstream industries. Block model analysis reveals the carbon emission transfer relationships and their interconnections among global industrial sectors, tending towards an industry clustering pattern where "production side" converges with "demand side" coexisting in supply and demand. There are noticeable inequalities in wealth gains and environmental burdens between these blocks. This paper can provide targeted carbon reduction policy recommendations for various industrial sectors to participate in global responsibility allocation and promote the formation of a low-carbon global industrial sector network.

Low-carbon city construction, spatial spillovers and greenhouse gas emission performance: Evidence from Chinese cities

Low-carbon cities (LCC) are conducive to low-carbon development and reshaping the urban economic growth model. However, it is still unknown whether it has a synergistic mitigation effect on other greenhouse gases (GHGs). In this study, a dataset comprising 283 Chinese cities spanning the period 2003 to 2019 is chosen. We employ spatial difference-in-difference (SDID) modeling to investigate both the impacts and mechanisms of LCC on GHG emissions performance. The results show that (1) LCC notably lowers local GHG emissions, enhances emission efficiency, and improves GHG emissions performance in neighboring cities within a 1000 km radius. (2) LCC indirectly enhances the GHG emissions performance of local and neighboring cities through energy intensity and green technology innovation. Notably, LCC boosts the local GHG emissions performance by industrial structure upgrading and resource allocation but harms the positive spillover effects on nearby cities due to the siphoning effect. (3) The effect and spatial impact of LCC on GHG emission performance is notably pronounced in eastern cities, non-resource cities, and key environmental protection areas. The results of the study will further promote the development of LCC and provide an important decision-making reference for urban low-carbon sustainability.

Does green finance boost carbon-neutral performance? Evidence from China

Carbon neutrality has attracted widespread attention as a key strategy for mitigating environmental degradation, but there is little research on whether the development of green finance can contribute to the process of carbon neutrality. This paper proposes a hypothesis based on the relationship between green finance and carbon-neutral performance and empirically tests it using a spatial Durbin model and combining it with a threshold panel model utilizing Chinese provincial sample data from 2011 to 2021. The study found that (1) green finance development would promote carbon-neutral performance; (2) there are notable spatial characteristics of green finance and carbon neutrality performance, with local green finance impacts both local and neighboring carbon-neutral performance; and (3) green finance impacts carbon-neutral performance at a single threshold and different levels of green finance development affect carbon-neutral performance differently. In the eastern, central, and western regions, the contribution of green finance to carbon-neutral performance gradually decreases. Thus, Chinese authorities should strengthen the green sustainable financing system, promote regional green finance, and enhance the carbon-neutral performance of green finance.