Narrow Results

This paper explores the applicability of the well-known “Pollution Haven” hypothesis and “Pollution Halo” hypothesis at the city-level in contemporary China. The fixed effect model and the threshold effect model are employed to investigate the relationship between foreign direct investment (FDI) and carbon emission in 265 Chinese prefecture-level cites. Based on the entire sample, the results of fixed effect model support the applicability of “Pollution Haven” hypothesis, while the hypothesis is substantiated primarily in eastern and western China. In contrast, the central part of China does not conform to either hypothesis, suggesting that the emergence of a distinct “Pollution Haven Basin” within the country. Unlike the inverted “U-shaped” curve commonly posited in the existing literature, the threshold effect model unveils a “U-shaped” connection between FDI and carbon emission. Specifically, we find that the turning point is at around $52.93 million, which implies that FDI needs to surpass a certain threshold to actively drive carbon emission in the presence of a “Pollution Haven Engine”. Accordingly, in the context of more in-depth international trade and China’s carbon neutralization commitment, it would be wise for the Chinese local governments to control the FDI scale, and encourage FDI to tilt toward the clean sectors.

Green finance directs social capital to support projects that benefit the environment and sustainable development. This paper constructs a policy analysis and evaluation (PAE) model based on a synthetic control method and policy instrument. Through the quantitative analysis of the actual effect of policy impacts and the analysis of policy instruments, the PAE model jointly analyses and evaluates the policy effects from both causes and consequences. This paper takes the first batch of green finance pilot zones in China as an example. The model results show that Guizhou has the best emission constraint effect. The carbon reduction effect in Jiangxi and Zhejiang is not significant. The growth of carbon emissions in Guangdong and Xinjiang accelerated. Furthermore, the PAE model analyses the mechanism of policy impacts. The difference in policy focus is the cause of the difference in carbon emission control effects. This result strengthens the reliability of the evaluation conclusions from the perspective of policy supply and provides valuable policy experience.

Process route planning directly influences carbon emissions, completion time, and the processing cost of mechanical products, and is crucial for achieving low-carbon, high-efficiency, and cost-effective machining production. To address this, an optimization method based on the fuzzy analytic hierarchy process (FAHP) and an adaptively improved ant colony algorithm is proposed. First, the manufacturing characteristics of the parts are analyzed, the work step element to represent them is introduced, and a carbon emission model for low-carbon manufacturing from the perspectives of material and energy flows is established. Additionally, an optimization model is constructed, targeting carbon emissions, completion time, and machining cost at the process level. To effectively address the fuzzy weight distribution among the optimization objectives, FAHP is employed to determine the weight of each factor and to define a comprehensive objective function. To enhance the solving efficiency of the optimization algorithm, an adaptively improved ant colony algorithm with multi-strategy fusion is utilized. Finally, the machining data of a part are employed as a test case to verify the feasibility and practicality of the proposed method. A comparison with the actual data indicates that, when low carbon, high efficiency, and low cost were treated as multi-objective optimization criteria, the carbon emissions were 1425.06 g, the processing time was 545.5 s, and the processing cost was CNY5.84. In comparison with the results from the other three experiments, the carbon emission, processing time, and processing cost exhibit the best overall performance, aligning with the low-carbon, low-cost, and sustainable production requirements.

Empirical studies on the effects of carbon emissions on population health are still in their infancy and its true implications have not yet been fully understood. The purpose of this study is to conduct a comparative analysis on the relationship between carbon emissions, energy consumption, income and public healthcare expenditure in Organisation for Economic Co-operation and Development (OECD) and non-OECD countries. The empirical research employs the dynamic common correlated effects of mean group (DCCEMG) and two-stage least square estimators. The findings indicate that carbon emissions increase healthcare spending only in non-OECD countries. The relationship between energy consumption and health expenditure varies significantly between OECD and non-OECD countries. Income increases health expenditure; however, the correlation is more robust in the OECD than in non-OECD countries. As a result, the findings recommend that non-OECD governments implement strategic environmental management policies that prioritize clean and healthy air to reduce healthcare costs.

This study uses the panel quantile regression model and conducts path analysis to examine the impact of inter-provincial digital economy development in China on carbon emission reduction. Results reveal several key points: First, the impact of digital economy development on carbon emission reduction varies significantly across quantiles; it can promote or inhibit emissions and shows an inverted “U” relationship. Second, the inhibition effect of the digital economy on carbon emissions is significantly stronger than the growth effect. Third, more than half of the studied regions show an inhibitory effect of the digital economy on carbon emissions, primarily through indirect impact paths. Among these paths, the three most significant ones are the improvement of urbanization level, resident affluence level, and per capita gross domestic product (GDP). Policy recommendations are discussed.

In the transportation system, an item is transported from sources to destinations. In this process, as well as the weight of the items volume of the items also plays a crucial role in obtaining minimum objectives. In general, one of these constraints becomes binding. Few transportation problems (TPs) have been formulated with the restriction on vehicle volume capacity. Surface transport is one of the most powerful carbon emitters, which leads to global climate change. Thus, in addition to the usual travel cost and time minimization, carbon emission (CE) minimization and maximum vehicle space utilization should also be considered. A TP for damageable and incompatible items in a multi-objective green 4-dimensional environment is presented in this paper. The model is developed with four objectives: minimizing transportation cost, transportation time, vehicles’ vacant space, and carbon emission amount. The model is solved by six multi-objective optimization methods, and their results are compared. A multi-criteria decision-making method, “MetaRanking,” is used to compare the results of the different multi-objective methods. The model is illustrated with some numerical data using the Generalized Reduced Gradient method through Lingo 11.0. A real-life example is also presented. Several particular models are deduced. The numerical results demonstrate the importance of each objective. Pareto fronts represent that each objective is conflicting with the other.

The relationship between economic growth, energy consumption, and carbon emissions has been extensively studied within the Environmental Kuznets Curve (EKC) framework. However, much of the existing literature focuses on the direct effects of energy consumption on the economic growth-carbon emission nexus, often overlooking the potential moderating influences of different energy sources. Thus, this study fills this gap by investigating the moderating effect of renewable energy consumption on the relationship between economic growth and carbon emissions in emerging economies using panel data methods. The study presents evidence supporting the existence of the EKC, indicating that economic growth negatively impacts the environment until a certain threshold of income is reached. Moreover, our results indicate that renewable energy weakens the relationship between economic growth and carbon emissions. This suggests that the adoption of renewable energy can help mitigate the negative environmental impacts of economic growth. Based on the findings, it is recommended that emerging countries should prioritize the integration of renewable energy into their energy mix, reduce dependence on fossil fuels, and promote economic growth strategies that align with environmental goals.

There has been a change in economic research from focusing on economic growth to focusing on sustainable development when it comes to economic development. This research aims to examine the link between China’s socioeconomic progress and the degradation of the environment. Between 1995 and 2018, the information in this study was collected. We looked at factors including the percentage of tourism in total exports, total and renewable energy consumption and GDP per capita when calculating the emissions they have on the environment. CO₂ emission shows long-term causation, but the feedback causality hypothesis holds in the short term between the variables of concern. There is a one-way correlation between GDP, CO₂ emissions and the use of renewable energy; access to better water, sanitation and electricity; access to renewable energy; and access to improved water, sanitation and electricity to CO₂ emissions. It was shown that CO₂ emissions, GDP and energy usage had a positive long-term correlation. In contrast, tourism, renewable energy and access to better water and sanitation all had a negative long-term correlation. GDP’s contribution to future CO₂ emission fluctuations ranges from 7% to 25%, with energy use at 7%–15%, renewable energy use at 1%–4.5% and tourism at 5.8%–10%. Meanwhile, access to better water, sanitation and electricity is at 9%, 1.13% and 3%, respectively, according to the variance decomposition analysis. Sustainable development in China necessitates a strong policy to adopt renewable energy sources, sustainable tourism growth and improved access to safe drinking water, sanitary facilities and power.

There is a coupling relationship between the development of urban transportation and cities: Urban growth leads to increase in the demand for urban transportation and consequently, a lot of transportation emissions. Therefore, an in-depth understanding of the mechanism behind the driving effect of urban development on transportation emissions is a crucial prerequisite for coordinated development of low-carbon urban transportation and cities. Based on the oil product allocation method, this paper estimates the transportation emission in Beijing from 1995 to 2016. Then based on the understanding of the driving mechanism, this paper applies the urban allometric scaling law to analyze the relationship between city size and transportation emission. Finally, the driving mechanism is analyzed using the STRIPAT model. The results reveal a superlinear relationship between transportation emission in Beijing and the expansion of the city, as the former outgrew the latter. Population size, urbanization, economic size, industrial structure, spatial scale and infrastructure construction are positive driving factors of transportation emission, whereas progress of energy technologies as a negative driving factor can restrain the growth of transportation emission. Urbanization has the most significant impact on urban transportation emission, and economic size contributes the most to the growth of transportation emission. Based on the results, we make a few policy recommendations for low-carbon urban transportation of Beijing, which include: improving transportation efficiency in the process of urbanization; promoting energy conservation and emission reduction while pursuing economic development so as to decouple transportation emission from urban development; restricting unordered urban expansion and updating the concept of transportation infrastructure supply; and developing energy technologies to improve energy efficiency.

China’s automotive industry has been dedicated to a series of carbon-reduction efforts and has strived for comprehensive green and low-carbon transformation in order to achieve carbon peaking and carbon neutrality goals. On the basis of automobiles’ whole life-cycle (WLC) carbon emission accounting, this paper calculates life-cycle carbon emissions per vehicle, proposes green and low-carbon development path for China’s automotive industry, quantifies and analyzes implicated carbon-reduction potential, and puts forth suggestions for high-quality green and low-carbon development of China’s automotive industry. The first is to establish a sound standard and data management system; the second is to promote research, development and application of low-carbon materials and low-carbon technologies; the third is to accelerate the pace of fostering a new consumption model in the automotive industry. The research results can further support national policy-making regarding carbon emissions, promote corporations’ research, development and application of low-carbon technologies, encourage green and low-carbon consumption, and lead the automotive industry to achieve WLC neutrality.

The Association of Southeast Asian Nations (ASEAN) region is vulnerable to the effects of climate change. However, urbanization and energy supply are essential to the ASEAN economy. This study examines the effects of CO₂ emissions and urban population on GDP per capita in 10 ASEAN countries from 1995 to 2021. Furthermore, ASEAN countries are divided into three income groups. According to the results of this study’s panel data analysis, urban population, and CO₂ emissions per capita have a positive effect on GDP per capita, respectively. However, both urban population and CO₂ emissions per capita reduce GDP per capita. While CO₂ emissions per capita and GDP per capita have a negative relationship in low- and high-income countries, the urban population positively impacts GDP per capita across the ASEAN region’s income levels. In low- and high-income countries, urban population, and CO₂ emissions per capita have a positive impact on GDP per capita.

This chapter aims to enhance knowledge of green finance in the existing literature and to review the sustainable development activities in the Middle East and North African countries. The study has also identified the challenges of providing green finance activities around the MENA region and offers suggestions to overcome the hindrances with the help of literary evidence. Research studies revealed that green finance and its related elements are at the forefront of discussion around the world. The reason behind these extreme changes in green nature is due to political, economic, social, and environmental fluctuations, which are affected by a fiscal crisis. Researchers have used the latest methodology of PRISM 2020, which is an advanced version of PRISMA 2009, for conducting a systematic literature review by considering 10 years of literature studies between 2013 and 2022 from the Scopus Search Engine to meet the research objectives of the study. This study would help future research activities on green finance to learn from the initiatives of MENA nations to understand the practical difficulties and opportunities available in society. It has remarkable implications for academic researchers, officials making policies, and entities providing various services to practitioners in the field of green nature as it is unique in understanding the growth and development of green finance activities and its sustainable development around the MENA region. The study concluded that green finance initiatives are possible by focusing on implementing various carbon emission proposals as part of sustainable programs. Government and financial institutions must consider that green finance activities enhance sustainable financing.

In response to the construction of a national carbon emission trading market, the corrected carbon emission amount of Aviation Industry of China (AVIC) was researched according to historical energy consumption data, and the carbon emission features were analysed from the perspectives of industries and production processes in this paper. In addition, the carbon emission reduction potentials are studied based on both total carbon emission and carbon intensity data. The research shows that the machinery manufacturing industry is the key emission reduction industry of AVIC, and its carbon intensity is higher than the industrial standard, with the reduction potential of 42.04%~61.36%. As to the production processes, the power station, test run and test flight, forging and machining are the major carbon emission processes. With high carbon emission reduction potential, there are 9 companies of AVIC which merit special attention in participating in carbon trading.