Environment, Development and Sustainability最新文献

Owing to real constraints, a first-best climate policy is rarely socioeconomically optimal; therefore, policymakers may prefer a second-best or mixed policy, where revenue recycling (RR) is usually implemented as a complementary policy to the first-best policy. Unfortunately, how different RR policies affect equality and efficiency during first-best policy implementation remains to be researched. This paper attempts to narrow the research gap by designing and evaluating the RR policies for the emission trading scheme (ETS) simulating the Chinese National Emission Trading Scheme (CNETS). To achieve this research target, we have employed a dynamic recursive computable general equilibrium (CGE) model to analyze how the designed RR policies complement the ETS effects on emission abatement and economic growth. The results of the CGE model have confirmed the existence of a tradeoff between equality and efficiency. RR for income tax reduction is beneficial to emission abatement, but it has the worst performances on equality, Gross Domestic Product (GDP), and household welfare. RR for subsidizing renewable energy causes the lowest GDP loss, but it adversely impacts emission abatement owing to the induced economic boom. Lump-sum income transfer to low-income households is the best RR option because it is the most equitable way to use ETS revenues and induces the highest household welfare with satisfactory performances on emission abatement and GDP. Hence, ETS revenues are recommended to be transferred to low-income households.

This study analyses how public attitudes toward climate actions have changed over time in some of the biggest CO₂-emitter countries representing two categories of economies: the rich and developed vs. emerging. Using the World Value Survey data and two-sample tests of proportions, an exploratory analysis is conducted to understand the change in climate change attitudes in China, the United States, India, Russia, Japan, Germany, and South Korea over the last three decades. The study finds initial evidence of divergence in public opinion for climate actions across countries. The findings show that people in emerging economies (such as China and India) have cultivated more favorable views toward environmental protection and climate actions over time. They have started demanding better environmental policies and shown willingness to contribute to environmental protection both monetarily and symbolically. However, people in the developed and rich world are gradually moving towards less favorable climate opinions. Such startling changes in public attitude have the potential to impact future national and global treaties on climate change disparagingly.

To realize low-carbon transition and pursue sustainable development, China’s central government formulated the low-carbon city pilot (LCCP) policy. Current studies focus primarily on the effect of the policy at the macro level of the cities, including economic, industrial, technological, environmental dimensions. So far, few has looked at the effect of the LCCP policy on residents’ welfare. To address this gap, this study treats the LCCP policy as an exogenous policy shock and employs the Difference-in-Differences model to examine its influence on residents’ welfare from the perspective of ecological welfare performance (EWP). Additionally, this study integrates the concept of quality of life into the EWP evaluation system to provide a comprehensive reflection of residents’ welfare. The results demonstrate that the LCCP policy significantly increase EWP in pilot cities, with a series of robustness tests support this finding. Besides, mechanism examination indicates that the LCCP policy enhances EWP through optimizing industrial structure, promoting low-carbon technological innovation, and increasing digital economy. Moreover, heterogeneous results reveal a significant increase of EWP in eastern cities, non-resource-based cities, and cities with high government environmental governance intensity due to the LCCP policy. This study empirically confirms the positive role of the LCCP policy in improving residents’ welfare, provides evidence for synergistic development of other countries seeking to advance low-carbon transition alongside improvements in residents’ welfare.

Empty pesticide containers (EPCs) are a source of high-quality high-density polyethylene with a few different colors and a practically constant quality over time; thus, EPCs are economically valuable and fully recyclable. There are two key aspects to the successful recycling of these containers, their cleaning and collection, the latter being especially challenging in areas where the distances between the generation nodes are large. This paper presents the development of a multi-period Mixed Integer Linear Programming model for the optimal design of a reverse logistics network for EPCs in large territorial areas and its application to the Buenos Aires province of Argentina. The model structure is based on the current legislation and reflects the interactions among society, development and the environment, allowing a quantification of the technical and economic implications of sustainable development. The proposed formulation takes into account investment and operating costs for each temporary collection center (TCC) and recycling plant, as well as transportation costs between nodes. The kilometers travelled to operate the network help to estimate the minimum number of vehicles required and the global climate change impacts of each studied alternative. Moreover, the model incorporates restrictions and operational alternatives commonly used in large territorial areas. This work is part of a broader project in collaboration with national agencies to develop tools to strengthen the political role and facilitate the implementation of the extended producer responsibility principle (EPR) in the context of EPCs management system. Moreover, the conclusions drawn from scenario studies serve as guidelines for its implementation in other countries or regions.

Energy transition from conventional centralized power plants, particularly coal-fired units, is critical for West Virginia’s long-term energy and economic future. The socioeconomic challenges faced by West Virginia are closely linked to its reliance on the centralized coal industry and economy, which has declined precipitously in the past decade. Many postindustrial communities in rural areas struggle to sustain economic viability, resulting in documented outmigration and diminished energy resilience. We investigated the possibility of introducing community-sized distributed energy systems in these rural communities to improve energy resilience and support the transition toward more sustainable energy production. This study investigated the feasibility of introducing community-sized distributed energy systems in rural West Virginia to enhance energy resilience and facilitate the transition away from traditional centralized energy. Utilizing a geospatial modeling approach with Multi-Criteria Decision Analysis (MCDA) and Geographic Information System (GIS) suitability assessment, we identified optimal locations for small-scale distributed wind, solar, and hydropower energy generation. The study conducted a net value comparison analysis, assessing the levelized cost of energy (LCOE) and levelized avoided cost of energy (LACE) to determine the economic feasibility of each distributed generation type compared to traditional coal-generated electricity. Our findings revealed that wind and solar distributed generation are most suitable in southern and eastern West Virginia counties, while potential sites for small hydropower development are dispersed across the state . This study offers valuable insights into the possible future of distributed energy and its infrastructure development in rural West Virginia, thus contributing to the state’s energy transition and economic revitalization efforts.

Corporate environmental information disclosure manipulation (EIDM) has a high level of concealment, which brings great challenges to the identification and judgment of manipulation behavior. Compared to traditional methods, machine learning techniques excel in handling large and complex datasets while achieving higher accuracy. This research applies machine learning techniques to construct the identification model of EIDM behavior and carry out the identification research of EIDM behavior. Based on the “public pressure” theory, the detection indicators will be improved from three aspects: public pressure, corporate governance, and financial indicators. By combining the collected environmental pollution penalty cases of Chinese listed companies from 2011 to 2020 with a pressure pool indicator system, we establish a training set and a test set to compare the identification ability of the logistic regression (LR), decision tree (DT), Support Vector Machine (SVM), Backpropagation (BP) Neural Network, and random forest (RF) models. Additionally, during the initial phase of model training, hyperparameter tuning is conducted across these models to ensure the maximization of their performance. For imbalanced data, after comparing the two oversampling techniques of the Borderline synthetic minority oversampling technique (Borderline SMOTE) and adaptive synthetic sampling (ADASYN), our study indicates that the Borderline SMOTE model has a better recognition effect than ADASYN and that the Borderline SMOTE-RF model is superior to the LR, DT, BP, and SVM models. We hope that our research can provide a reference for regulatory authorities, accelerate the improvement of the mandatory environmental information disclosure (EID) system of listed companies, improve the identification and early warning capabilities of EIDM, and promote the improvement of EID quality.

The connection between land urbanization (LU) and the ecosystem service value (ESV) from land is studied theoretically and empirically in this research. Firstly, an optimization model with constraints in which the externality of land development and construction is internalized into decision-making behavior of local governments is established to theoretically find out the potential link between urbanization and ecosystem value; then, an empirical analysis using cross-section dataset of 280 cities in mainland China in 2010 is conducted to verify the theoretical results. It could be concluded that the influence of LU on ESV of land depends on phase of economic development. When the unit conveyance fee’s marginal effect of requisitioned land is greater than the unit compensation’s marginal effect of this land, LU exerts a negative impact toward ESV of land and vice versa. An empirical analysis indicated a U-shaped curve between LU and the ESV of land, biodiversity, carbon sequestration, soil retention, and water retention. However, the effect of LU on ESV is currently still negative. These results held up significantly after endogeneity tests and some robustness tests. Finally, we suggest that Chinese government should promote innovations in terms of the pattern of land use as well as institutions.

Since 2014, China’s economy has transitioned from rapid growth to the new normal, and the challenges of resource shortages and environmental pollution remain prominent. Therefore, China’s contemporary economic development objective is no longer to pursue high-speed growth alone but to achieve high-quality development. Green development is an internal requirement and a crucial choice for high-quality economic development. As the most distinctive and universal geographical distribution feature of economic activities, industrial agglomeration has a broad and profound impact on industrial and regional economies and the advancement of green development. This study examines 27 manufacturing industries in China to investigate the influence of agglomeration on industrial ecological efficiency. First, we empirically tested this influence from an overall perspective. We then divide manufacturing industries into technology-, capital-, resource-, and labor-intensive industries. This study constructs linear regression and threshold models to empirically analyze the divergence in influence effects among the four types of industries. The results reveal that the average influence of the manufacturing industrial agglomeration on industrial ecological efficiency is an inverted N-shaped threshold effect. Notably, the agglomeration of technology-intensive industries significantly promotes ecological efficiency. By contrast, resource-intensive industry agglomeration exhibits a restraining effect. Finally, labor- and capital-intensive industry agglomerations present prominent threshold effects, revealing inverted N-shaped and U-shaped nonlinear relationships, respectively. The conclusions of this research can serve as a reference for the policy formulation of green industrial development in China and other transitional economies.

China has experienced rapid economic growth and industrialization over the last 70 years, which has created significant opportunities for firms and improved the quality of life for many residents. However, this increased industrialization has also had a significant negative impact on the environment. To address this major challenge, the Ministry of Environmental Protection (MEP) of the People’s Republic of China has introduced several Environmental Protection Regulations (EPRs) into its regulatory framework during the last 2 decades in order to reduce the pollutant emissions generated by firms. Based on panel data from publicly listed firms in China’s manufacturing industry, spanning 2010 to 2020, this study aims to assess the impact of EPRs on firms’ pollutant emissions and resultant Research and Development (R&D) investment. In addition, the research examines the contributory role of R&D investment in the mitigation of pollutant emissions, providing important insights for governments and policy makers when seeking to refine environmental policies. In so doing, several noteworthy contributions are made to current literature: first, newly enacted EPRs are found to have markedly decreased both the aggregate and per-output pollutant emissions of firms. Second, there has been a significant upsurge in R&D investment prompted by the introduction of the new EPRs. Third, the study did not confirm a significant mediating role for firms’ R&D investments in reducing pollutant emissions; this conclusion was supported by robustness testing. Fourth, the EPRs were found to notably reduce pollutant emissions for state-owned firms and those in economically advanced regions. However, for firms in less developed regions and those not state-owned, EPRs could potentially reduce their revenues.

COVID-19 brought tremendous disruption to daily life, accelerated urban digital transformation, and prompted sustainable improvement in socio-spatial development. The emergence of analytic technologies stimulates possibilities for integrating socio-spatial relationships, and new urban research methods and assessment systems are required, which are rarely discussed in recent studies. To bridge the research gap, this study proposes a measurement framework that combines Location Based Social Networking Services (LBSNs) and POI data to test the effectiveness of urban regeneration projects. Specifically, the study quantifies urban vitality to evaluate people’s preferences and place settings integrating by objectively utilizing LBSNs with social media (Weibo check-in and Dianping data) and POI data. This study focuses on 153 regeneration projects in Suzhou, China, from 2019 to 2021 and categorizes urban regeneration projects into four types: (1) community, (2) commercial, (3) public space, and (4) industry. Further, a binary logistic regression model was conducted to analyze the effects of different regeneration measures on urban spatial vitality. The research findings show that combining LBSNs data with POI data can effectively assess the transformation in urban vitality with spatiotemporal analysis. The results indicate that commercial and community regeneration projects significantly impacted urban vitality, and cultural-related renovation measures are critical in improving urban vitality. The systematic urban regeneration measurement framework could promote the regeneration efficiency and rationalization of decision-making, providing a basis for understanding complex socio-spatial relationships and ensuring sustainable urban developmeng sustainable urban development significantly.