Lijie Gao

As an economically active region, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) faces dual challenges of carbon and air pollution reduction. Existing studies predominantly focus on single pollutants or engineering pathways, lacking systematic analyses of multi-scale synergistic effects. This study investigates the spatiotemporal distributions, driving factors, and synergistic effects of CO₂ and volatile organic compounds (VOCs) at the multi-scale of urban agglomerations, cities, and industries, using global Moran’s index, standard deviational ellipse, logarithmic mean divisa index decomposition model, and Tapio decoupling model. The results show that the average annual growth rate of CO₂ (7.4%) was significantly higher than that of VOCs (4.5%) from 2000 to 2020, and the industrial sector contributed more than 70% of CO₂ and VOC emissions, with the center of gravity of emissions migrating to Dongguan. Industrial energy intensity improvement emerged as the primary mitigation driver, with Guangzhou and Shenzhen demonstrating the highest contribution rates. Additionally, CO₂ and VOC reduction show two-way positive synergy, and the path of “energy intensity enhancement–carbon and pollution reduction” in the industrial sector is effective. Notably, the number of strong decouplings of the economy from CO₂ (11 times) is much higher than the number of strong decouplings of VOCs (3 times), suggesting that the synergy between VOC management and economic transformation needs to be strengthened. This study provides scientific foundations for phased co-reduction targets and energy–industrial structure optimization, proposing regional joint prevention and control policy frameworks.

County is the center of China’s socio-economic development and the key node for urban–rural integration. Also, the county is an important carrier for promoting urban and rural green development. Improving green and low-carbon development capabilities and formulating county-level low-carbon standards will play a significant role in promoting China’s new people-oriented urbanization and rural revitalization. Although there have been extensive studies on low-carbon benchmarks, over half of the benchmarks tend to ignore the development stage of the evaluated region and its needs. When the region’s economy reaches a certain level, constraints from low-carbon targets may limit the local development process. This study firstly allocated county carbon intensity reduction targets (CIRT) by considering the differences in county carbon reduction capacity and responsibility. Secondly, a dynamic benchmark system of per capita carbon emissions (PCCE) in counties in China is constructed. Finally, we took Changxing County in Zhejiang Province as a research case to verify the dynamic benchmark of PCCE. According to the carbon intensity target reduction rate (CITRR), China’s counties can be divided into three categories: low carbon emissions reduction capability-responsible counties (L-CERCRC), medium carbon emissions reduction capability-responsible counties (M-CERCRC), and high carbon emissions reduction capability-responsible counties (H-CERCRC). The results show that (1) due to the national CO₂ emission reduction target in 2030, the carbon intensity will be 60% lower than in 2005, the CITRR for China’s 1510 counties range from 8.36 to 137.83%; the average CITRR is 48.40%. (2) Changxing County’s CITRR is 57.71%, which belongs to the H-CERCRC. The PCCE of Changxing County will be much higher than the benchmark when the carbon peak is reached in the future. (3) For reaching the aiming benchmark, Changxing County is suggested to adjust its relevant influencing factors of PCCE for converting local’s PCCE reaching to the benchmark within a certain time period. The dynamic benchmark system for PCCE in China’s counties established in this study is economically sensitive, which not only takes the differences of counties into account, but also meets the needs of counties’ diverse development form stages. This system provides counties a few coordinated directions which can improve the local’s economic development and reduce greenhouse gas (GHGs) emissions through the development progress.

As the most basic unit of the national economy and administrative management, the low-carbon transformation of the vast counties is of great significance to China’s overall greenhouse gas emission reduction. Although the low-carbon evaluation (LCE) indicator system and benchmarks have been extensively studied, most benchmarks ignore the needs of the evaluated object at the development stage. When the local economy develops to a certain level, it may be restricted by static low-carbon target constraints. This study reviews the relevant research on LCE indicator system and benchmarks based on convergence. The Environmental Kuznets Curve (EKC), a dynamic benchmark system for per capita carbon emissions (PCCEs), is proposed for low-carbon counties. Taking Changxing County, Zhejiang Province, China as an example, a dynamic benchmark for PCCEs was established by benchmarking the Carbon Kuznets Curve (CKC) of best practices. Based on the principles of best practice, comparability, data completeness, and the CKC hypothesis acceptance, the best practice database is screened, and Singapore is selected as a potential benchmark. By constructing an econometric model to conduct an empirical study on Singapore’s CKC hypothesis, the regression results of the least squares method support the CKC hypothesis and its rationality as a benchmark. The result of the PCCE benchmarks of Changxing County show that when the per capita income of Changxing County in 2025, 2030, and 2035 reaches USD 19,172.92, USD 24,483.01, and USD 29,366.11, respectively, the corresponding benchmarks should be 14.95 tons CO₂/person, 14.70 tons CO₂/person, and 13.55 tons CO₂/person. For every 1% increase in the county’s per capita income, the PCCE allowable room for growth is 17.6453%. The turning point is when the per capita gross domestic product (PCGDP) is USD 20,843.23 and the PCCE is 15.03 tons of CO₂/person, which will occur between 2025 and 2030. Prior to this, the PCCE benchmark increases with the increase of PCGDP. After that, the PCCE benchmark decreases with the increase of PCGDP. The system is economically sensitive, adaptable to different development stages, and enriches the methodology of low-carbon indicator evaluation and benchmark setting at the county scale. It can provide scientific basis for Chinese county decision makers to formulate reasonable targets under the management idea driven by evaluation indicators and emission reduction targets and help counties explore the coordinated paths of economic development and emission reduction in different development stages. It has certain reference significance for other developing regions facing similar challenges of economic development and low-carbon transformation to Changxing County to formulate scientific and reasonable low-carbon emission reduction targets.

Pollutants discharged from irrational energy consumption pose a serious threat to urban ecological security. The Western Taiwan Straits Economic Zone is an important part of China’s coastal economy. With the rapid development of the economy in this area, the atmospheric environmental pollution problem, caused by energy consumption, has become increasingly serious. Therefore, the study of the environmental impact assessment of air pollution in the Western Taiwan Straits Economic Zone has reference value to prevent ecological risks. This paper constructed a regional-scale environmental impact assessment model that includes pollution sources, pollution stress, and evaluation results, and evaluated the environmental impact of SO₂, NO₂, CO, PM₁₀, and PM_2.5 from three perspectives: regional integration, different energy consumption sectors, and different cities. The results showed that the regional environmental impact level of the research area was high, and the main pollutants transformed from SO₂ to NO₂, PM₁₀, and PM_2.5 from 2008 to 2016. According to the results of different sectors, the transportation sector contributes the most to NO₂ and remains unchanged, and the industrial sector contributes the most to SO₂, PM₁₀, and PM_2.5. Combined with the research results of different cities, cities concentrated in the coastal areas contribute more pollution than other cities do.

The regional allocation of carbon emission quotas is of great significance to realize the carbon emission target. Basing on the combination of the multi-index method and the improved equal-proportion distribution method, and fully considering the differences in economic factors, population factors, energy factors, technological factors among cities, China’s 2030 carbon intensity reduction target was allocated. The results indicate that: (1) Under the target constraint of 60% reduction in CO₂ emissions per unit of Gross Domestic Product (GDP) (carbon intensity) in 2030 compared to 2005, the carbon intensity target reduction rate (CITRR) of 285 Chinese cities is between 17.65% and 141.14%, with an average reduction rate of 51.52%; (2) the CITRR of cities presents significant spatial positive correlation, and the Global Moran I correlation index is 0.38; and (3) the distribution trend of CITRR is the same as the general trend of economic development of China, showing a basic trend of gradual decline from south to north and from coastal to inland. The allocation method takes into account fairness and efficiency, and reflects the differences between cities, so that the allocation results are likely to be accepted by all parties. Meanwhile, this method breaks the limitation of the lack of city’s data and is likely to implement in actual operation. Cities should choose distinguished low-carbon economic development paths, in combination with their characteristics of economic and social development, and carry out inter-city cooperation to promote carbon emission reduction steadily.

Sustainable development (SD) has become a fundamental strategy to guide the world’s social and economic transformation. However, in the process of practice, there are still misinterpretations in regards to the theory of SD. Such misinterpretations are highlighted in the struggle between strong and weak sustainable development paths, and the confusion of the concept of intra-generational and inter-generational justice. In this paper, the literature survey method, induction method, and normative analysis were adopted to clarify the gradual evolution and improvement process of the concept and objective of SD, to strengthen the comprehensive understanding of the SD theory. Moreover, we also tried to bring in the situation and concepts of China. The results show that the theory of SD has gone through three periods: the embryonic period (before 1972), the molding period (1972–1987), and the developing period (1987–present). SD is gradually implemented into a global action from the initial fuzzy concept, including increasing practical wisdom. The goal of SD evolves from pursuing the single goal of sustainable use of natural resources to Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs). This paper argues that the theory of strong sustainability should be the accepted concept of SD. Culture, good governance, and life support systems are important factors in promoting SD.

Spatial information can be integrated into almost all fields of industrial ecology. Many researchers have shown that spatial proximity affects a variety of behaviors and interactions, and thus matters for materials stocks and flows analysis. However, normal tools or models in industrial ecology based on temporal dependence cannot be simply applied to the case of spatial dependence. This paper proposes a framework integrating material stocks and flows analysis with spatial analysis. We argue that spatial analysis can help data management and visualization, determine spatio-temporal patterns-processes-drivers, and finally develop dynamic and spatially explicit models, to improve the performance of simulating and assessing stocks and flows of materials. Scaling in spatial, temporal, and organizational dimensions and other current limitations are also discussed. Combined with spatial analysis, industrial ecology can really be more powerful in achieving its origin and destination—sustainability.

Suburban roads are an important part of China’s road network and essential infrastructure for rural development. Poorly designed road curves and scarcity of traffic signs have caused an excessively high traffic accident rate in plain topographical areas. In this study, an approach to evaluate and improve rural road traffic safety is introduced. Based on fuzzy and cask theory and weighted analysis, a cask evaluation model is built. It provides a quantitative instant method for analyzing road safety in the absence of traffic accident information or rigorous road space data, by identifying dangerous sections and key impact factors, and ultimately help to put forward traffic safety improvements. Based on the application to a specific section of Xiaodang Central Road in the Fengxian District of Shanghai, the result shows that the pavement conditions of cement-hardened dual-lane rural roads was good, but traffic safety was poor. Missing traffic signs, unreasonable road alignment, and poor roadside conditions were the main problems. Finally, improvements of the short-stave subsystem were proposed: the location of guide signs and roadside conditions should be improved, and the number and efficacy of the rural road traffic signs need to be increased, and markings should be and receive regular maintenance.