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Alcohol fuels with different carbon numbers such as propanol (C3), butanol (C4) and pentanol (C5) have lately become popular in both conventionally and RCCI-operated diesel engines thanks to their high cetane number (CN) and oxygen content along with lower latent heat of evaporation, which are useful for reducing the high CO/HC emissions, whereas RCCI mode still suffers from these emissions. Therefore, in this study, these three alcohol fuels with carbon numbers ranging from C3 to C5 were employed as low-reactivity fuel (LRF) in a single-cylinder RCCI engine under a constant engine speed of 2400 rpm and varying loadings (from 20 to 60% of full load at 20% intervals) and premixed ratios (from 0 to 60% with 15% intervals) when using B7 as high-reactivity fuel (HRF). In the experimental study, the effect of oxygen content, latent heat of evaporation, and cetane number which changes linearly with the carbon number of alcohols used, on exhaust emissions, were analyzed. When compared to conventional diesel mode (CDM), RCCI mode using alcohol fuels increased HC and CO emissions but decreased smoke opacity at a great level. The greatest reduction in both CO emissions and smoke opacity was recorded with the use of propanol which has the highest oxygen content, while pentanol with the lowest latent heat of evaporation had success in reducing HC emissions. In addition, NOx emissions were reduced by up to 60% when butanol was used as low-reactivity fuel. Ignition delay increased more during RCCI operation with propanol having an octane number of 118, whereas butanol and pentanol have 92 and 78, respectively. It was concluded that the properties of alcohol fuels such as the oxygen content and latent heat of evaporation had a significant effect on HC/CO emissions in RCCI engines., Competing Interests: Declarations. Ethical approval: Not applicable. Consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The study analyzes the impact of environmental regulations on carbon emissions in countries with different levels of emissions, utilizing two measures of carbon emissions based on: production (PBA) and consumption (CBA) accounting. Environmental regulations are measured by means of three components of the Environmental Policy Stringency (EPS) index: market-based and non-market-based instruments, and technology support. The Moments-Quantile Regression method is employed to assess the effectiveness of these policies across countries with varying levels of emissions-high, medium, and low within the Environmental Kuznets Curve. The findings indicate that increased stringency in environmental regulations correlates with reduced carbon emissions per capita. Notably, the EPS index has a more significant effect on reducing PBA emissions compared to CBA emissions. A key finding is that the EPS index is more effective in countries with lower pollution per capita (i.e., lower quantiles) than in those with higher pollution per capita. Among the three components, market-based instruments are identified as the most effective in reducing carbon emissions. Additionally, in countries where per capita emissions are relatively low, the combination of market and non-market instruments proves to be the most effective in reducing emissions. In contrast, the highest carbon emitters per capita tend to achieve emissions reductions primarily through technological support., Competing Interests: Declarations. Competing interests: The authors have no relevant financial or non-financial interests to disclose. Ethics approval and consent to participate: Not applicable. Consent for publication: The authors are willing to permit the Journal to publish the article., (© 2024. The Author(s).)

This study investigated the size-segregated carbonaceous components of particulate matter (PM), including ultrafine particles (UFP or PM 0.1 ), across three distinct urban settings in Phnom Penh, Cambodia: an educational site (University of Health Sciences, UHS), an institutional site (Ministry of Environment, MoE), and a residential area near a landfill. A cascade impactor-type sampler equipped with an inertial filter was used to collect size-segregated particles down to UFPs. Carbonaceous species, including organic carbon (OC) and elemental carbon (EC), were analyzed using a thermal/optical carbon analyzer to determine their composition and ratios. The results indicated no significant differences in mass concentration between weekdays and weekends at all sites; however, PM profiles varied across the location. The UHS site displayed higher EC levels in UFP, with concentrations suggesting significant local vehicular emissions, underscored by a lower OC/EC ratio (2.39 ± 1.13) compared to other sites. In contrast, the landfill site exhibited higher OC components in finer particles, suggesting emissions from organic sources such as waste burning and cooking activities, evidenced by higher OC/EC ratios across all particle sizes (e.g., OC/EC ratio in UFP at 3.78 ± 0.98). The MoE site presented a balanced profile with moderate levels of both OC and EC, influenced by its proximity to natural dispersion factors like the Tonle Sap River. Additionally, air mass backward trajectory analysis integrated with hotspot data indicated transboundary influences, particularly from agricultural burning in surrounding provinces, including Vietnam. Therefore, both local and transboundary emissions influenced the PM levels in Phnom Penh city., Competing Interests: Declarations. Ethical approval: This study did not include any tests on clinical samples or animals, so the related considerations are not applicable. Consent to participate: All authors of the article agree to participate in journal submission. Consent for publication: All authors approved the publication of this manuscript. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Carbon emissions from urban transportation significantly contribute to overall transportation emissions and are a major cause of the continuous rise in global temperatures. Understanding the spatial distribution and influencing factors of carbon emissions from operating vehicles can aid in formulating targeted policies and promoting emission reduction. To analyze the factors influencing urban traffic carbon emissions, we calculated emissions using trajectory data from operating vehicles in Shenzhen. We then used gradient boosting regression tree methods, specifically RF, XGBoost, and LightGBM models, to analyze the impact of the built environment on vehicle emissions. We used the XGBoost model for detailed factor analysis by comparing the models. The results indicate that bus stops, intersections, housing density, metro stops, and land use mix are the top five factors influencing emissions. When road density is 0-15 km/km 2 , the distance from the city center is 0-6 km, and the population exceeds 2000/km 2 , the built environment significantly reduces vehicle emissions., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In this study, we investigated the temporal variation of organic and inorganic aerosol with its optical properties in Mumbai (India), an urban coastal region. Mean PM 2.5  concentrations during the sampling period were 175 μg/m 3 (winter) and 90 μg/m 3 (summer). During winter, the average concentrations of organic (OC), elemental (EC), and water-soluble organic carbon (WSOC) were three times higher than in summer. Secondary organic carbon (SOC) contribution in OC was higher in summer (78%) than in winter (53%), and strong solar radiation in summer likely caused this outcome. Aerosols were slightly acidic in both seasons, with an average pH of 5.7 (winter) and 6.0 (summer). A correlation was observed between SOC and the acidity of particles in summer (R 2  = 0.6), indicating some amount of acid-catalysed SOC formation. In both seasons, the sulphate oxidation ratio (SOR) was higher than the nitrate oxidation ratio (NOR), which may reflect a preference for SO 2 oxidation over NO 2 or the difference in partitioning ammonium nitrate into ammonium sulphate under high RH. The dominant mechanism of SOC formation (gas vs aqueous phase oxidation) also showed seasonal variation. In winter, a relatively steep reduced major axis (RMA) slope of O 3 /CO suggests gas phase oxidation was the dominant mechanism of SOC production. Winter has more BrC fraction than summer, indicating higher absorbing aerosols, though the efficiency of absorbing the light was higher in summer. To assess the radiative forcing of PM 2.5 on a local scale, an effective carbon ratio (ECR) was computed. The findings pointed to a local radiative heating impact caused by PM 2.5 . The spectral slope ratio and MAE at 250 to 300 nm ratio (E 2 /E 3 ) revealed a higher abundance of high molecular weight species in WSOC during summer than in winter., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study evaluates the composition and seasonal characteristics of fine particulate matter (PM 2.5 ) during winter and summer through simultaneous measurements conducted at the Gwangju Institute of Science and Technology in South Korea and the Changping campus of Peking University in China. PM 2.5 samples were concurrently collected at both sites, and chemical analyses were conducted to quantify various components, including carbonaceous materials, ionic species, and metals. Although the average PM 2.5 concentrations were comparable between the two sites, there were distinct differences in the concentrations of major components. Organic indicator compounds were analyzed to discern the contributions of primary and secondary pollution sources. Changping displayed a mix of primary and secondary pollution, characterized by higher concentrations of primary organic carbon (POC) such as polycyclic aromatic hydrocarbons and hopanes, compared to Gwangju. In contrast, Gwangju demonstrated a higher prevalence of secondary organic carbon (SOC), particularly water-soluble organic carbon not related to biomass burning (WSOC nbb ) and various polar organic compounds. The organic mass to organic carbon (OM/OC) ratios estimated using the mass balance method revealed significant differences, with Gwangju showing a higher ratio of 2.3 compared to 1.9 at Changping, indicating a greater influence of secondary pollutants at Gwangju. Additionally, both Changping and Gwangju exhibited higher OM/OC ratios in summer (Changping: 2.0, Gwangju: 2.5) compared to winter (Changping: 1.8, Gwangju: 2.2), indicating seasonal differences in organic mass contributions to PM 2.5 . These findings underscore the importance of accounting for spatial and seasonal variations in air pollution studies and suggest that updating commonly used OM/OC ratios could enhance the reliability of research outcomes., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In 2020, China pledged carbon reduction targets at the United Nations: peaking emissions by 2030 and achieving carbon neutrality by 2060. Research and prediction of regional carbon emissions are crucial for achieving these dual carbon targets across China. This study aims to construct an indicator system for regional carbon emissions and utilize it for forecasting. Analyzing carbon emission data from a specific area in Hainan Province from 2010 to 2020, we established an indicator system. Using the interpretable SHAP model, we assessed indicator importance and trends. Employing an improved STIRPAT model with partial least squares regression to address multicollinearity among influencing factors, we developed a carbon emission prediction model. Based on this, we forecasted carbon emissions from 2021 to 2060 in the specified area under three scenarios: natural, baseline, and ambitious. The results show that the growth of resident population and per capita GDP has the most significant promoting effect on carbon emissions in the region while optimizing industrial structure, energy consumption structure, and reducing energy intensity will inhibit carbon emissions. The prediction results indicate that in the natural scenario, regional carbon emissions will peak in 2035, and achieving carbon neutrality by 2060 is not feasible, while the baseline scenario and ambitious scenario can achieve the dual carbon targets on time or even earlier. The research results of this article provide a reference method for predicting carbon emissions in other regions and a guide for future regional emission reduction., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Achieving carbon neutrality has become a global common goal. For China, to reach peak carbon emissions and long-term carbon neutrality, the transformation and development of resource-based cities are essential. This study uses data from 114 prefecture-level resource-based cities from 2008 to 2019 as a sample and empirically tests the impact of industrial land mismatch on carbon emissions using the fixed effects model. In addition, we analyze the heterogeneous influence of environmental regulation as a moderating effect on resource-based cities at different development stages. The study reveals that (1) there is a significant positive correlation between the imbalance in industrial land supply in resource-based cities and carbon emissions. The more severe the imbalance, the higher the carbon emissions. The improper supply mode of industrial land is also positively correlated with carbon emissions, although the impact is not significant. (2) Environmental regulation can significantly curb the carbon emission issues caused by the mismatch and imbalance in the scale of industrial land supply and the improper supply mode of industrial land. (3) Compared to strong resource-based cities, weak resource-security cities have a smaller impact on carbon emissions due to an imbalance in the supply of industrial land. This is mainly because resources in weak resource-security cities are becoming exhausted, making "ecology first, green and low carbon" the main tune for economic and social development. Both types of cities show a positive correlation between the improper supply of industrial land and carbon emissions, although neither is significant. (4) The intensity of the regulatory effect of environmental regulations on resource-based cities is influenced by resource abundance. The suppression of carbon emissions by environmental regulations is more apparent in strong resource-security cities than in weak resource-security cities., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In China, urban sprawl and developed land expansion challenge the country's "carbon peak" and "carbon neutrality" goals. Counties as the basic governance units are crucial for effective carbon reduction policies. This study examines land use carbon emissions (LUCE) in Shaanxi Province at the county level, essential for China's low-carbon strategy. Analyzing data from 107 counties between 2000 and 2020, we found that developed land, though increasing, is the primary carbon source with a slowing growth rate. The Conversion of Cropland to Forests and Grasslands national policy mitigated the impact on carbon absorption. Carbon emissions displayed positive autocorrelation and spatial heterogeneity, varying across the region. Using the Spatial Durbin Error Model, we linked county-level emissions to GDP per capita, population, urbanization rate, and research and development expenditure for direct and indirect influence. These factors correlate with fossil fuel use and high-quality industrial development. Promoting public transits and reducing private car use are vital for achieving local and regional low-carbon goals., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Urban form is a key factor affecting carbon emissions. Accurately estimating the impact of urban form on the carbon emissions of residents (CER) is an important prerequisite for China to adopt effective low-carbon spatial planning strategies and respond to climate change. However, counties mainly account for China's energy consumption, and the relationship between their urban form and the CER remains unclear, limiting their low-carbon development. Therefore, in this study, the PLS-SEM model and data from 90 counties in the Yangtze River Delta are used to determine the extent and ways that urban form affects the CER. The model considers the impact of both geometric-aspect urban form factors (urban scale, compactness, spatial structure, and urban shape) and built environment-aspect urban form factors (public service facilities, urban greening, road traffic, public transportation, and urban energy infrastructure) on the CER. The results indicate that urban form factors related to the built environment (public service facilities, urban greening, road systems, and municipal infrastructure) have a direct impact on the CER. Geometric-aspect urban form factors (size, compactness, spatial structure, and shape) not only directly affect the CER but also indirectly affect the CER by altering built environment-aspect urban form factors. This study identifies the complex relationship between urban form and the CER, facilitating the coordinated integration of multiple elements and providing a basis for the formulation of low-carbon spatial planning strategies for counties., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Despite the abundance of research on reducing carbon emissions, there is a significant gap in understanding the influence of macroeconomic factors on carbon dioxide (CO 2 ) emissions from a spatial-structural perspective. This study aims to contribute to the literature by investigating the impact of macroeconomic factors on carbon dioxide emissions in six East African countries between 1989 and 2020. Using spatial econometric panel models, the study analyzed spatial dependence among the variables. The empirical findings indicate that gross domestic product (GDP) per capita and electricity consumption have positive direct and indirect effects on carbon emissions, while fuel prices and exports have negative direct effects, but positive spillover effects on neighboring countries. Imports have a positive impact on local economies, but negative spillover effects. Additionally, the urban population has no significant impact on the environment. These findings provide important policy implications for optimizing spatial growth patterns and achieving a low-carbon economy in East African countries., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Submarine groundwater drainage (SGD) changes the elemental composition of the neighboring coastal ocean and impacts the biogeochemical cycles. To examine the seasonal and spatial variability in dissolved organic carbon (DOC) and labile organic compound biochemical compounds like dissolved carbohydrates (TDCHO), dissolved proteins (TDPRO), and dissolved free amino acid (TDFAA) concentrations during the dry and wet periods, groundwater samples were taken at 90 locations (180 samples) along the Indian coast. The mean DOC contents in Indian coastal groundwaters were more significant than the global mean values. DOC, TDCHO, TDPRO, and TDFAA concentrations are higher during wet than dry periods. The DOC and labile organic compound showed a substantial positive association with soil organic carbon, and respective labile compounds in soil, population, and land usage and poor relation with woodland territories, implying that soil organic compounds leaching is a source of DOC and other labile organic compounds into the groundwater. DOC and other labile compounds concentrations were linearly associated with population density, land usage, and sewage production, demonstrating that anthropogenic activities tightly regulate the formation of DOC in groundwater. During the wet and dry periods, total labile organic compounds (TDCHO, TDFAA, and TDPRO) constituted 21% and 10.5% of DOC, respectively. Compared to the wet time, more aromatic compounds accumulated during the dry season but were less bioavailable. SGD DOC flux contributed 2-7% of riverine DOC flux to the coastal ocean. The SGD flux from the Indian subcontinent to the nearby northern Indian Ocean accounts for approximately 2% of the worldwide SGD flux. The effect of DOC flux via SGD on coastal bacterial activity, the plankton food web, and the oxygen minimum zone must be studied., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Exploring the coupling coordination degree (CCD) between digital village construction (DVC) and agricultural carbon emissions (ACE) is crucial for promoting village revitalization and sustainable agricultural development. Analyzing data from 30 provinces in China in 2011-2020, this paper employs the CCD model, the Dagum Gini coefficient, and the geographic detector for in-depth analysis. The results show that the overall level of CCD gradually increases over time, but the national CCD still remains in a state of "low coordination," and there are apparent spatial differences in the CCD among provinces. In addition, the overall difference in CCD shows a decreasing trend, and the contribution of inter-regional differences has gradually become the most critical source of CCD's regional difference. Finally, the spatial differences of CCD are the result of two-factor interaction, among which the innovation level is the most core driving factor. The above findings provide important implications for China and other developing countries to fully leverage the interaction between village digitalization and agricultural decarbonization to promote sustainable agricultural development., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In the context of China's green development and "dual carbon" goal, urbanization, as a way to achieve Chinese modernization, has a particularly important effect on green and low-carbon economic development. Firstly, this paper empirically analyzed the influence of urbanization on per capita carbon emissions using Chinese city data and a panel fixed-effects model. Then, the impact mechanisms of urbanization on carbon emissions were examined from both the demand and supply sides. Finally, we analyzed the differences in the transmission mechanisms of urbanization affecting carbon emissions in the eastern, central, and western regions. The results show that (1) urbanization increases per capita carbon emissions. However, this effect shows inter-regional differences, with more significant promotion effects in the eastern and central regions; (2) on the demand side, the residents' consumption intensity can drive carbon emissions, while the rise of human capital agglomeration suppresses carbon emissions; on the supply side, industrial structure can drive carbon emissions, while the increase of green technological innovation suppresses carbon emissions; (3) the consumption effect and the industry effect play a major role in the eastern and central regions, while the intermediary effect is not obvious in the western region. This study can provide important insights for synergizing urbanization and achieving carbon reduction commitments., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study examines the nexus between "stock returns," carbon emissions, and "environmental tax" in 28 OECD countries for the period 1994 to 2014. To this end, we employ second-generation econometric techniques that are robust to macroeconomic and financial datasets to eliminate the issues associated with heterogeneity and cross-sectional dependence. The "common correlated effects mean group (CCEMG)" and "augmented mean group (AMG)" estimators are a pioneering attempt to explore the association between the cross sections. These approaches take into account the complexities of real-world data and provide a more accurate understanding of the relationship between the variables. Several studies explored the relationship between CO 2 emissions and economic growth. However, the literature lacks studies examining the impact of "environmental tax" implementation on "stock returns," which is a central policy instrument to curb emissions. The results demonstrate a significant and negative relationship between CO 2 emissions and "stock market index returns" and a positive and significant relationship between "environmental tax" and "stock market index returns." Furthermore, both these relationships prevail in the world's biggest financial markets and second largest CO 2 emitters, the USA. The empirical findings offer various useful implications for investors, policymakers, brokers, corporations, governmental pollution abatement institutions, and other stakeholders who wish to obtain a carbon risk premium., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A population is regarded as the main non-economic driver of carbon emissions, causing the climatic crisis, especially in China experiencing a dramatic demographic transition. In contrast to aging, low fertility, the most remarkable feature of the Chinese population transition, has always been ignored when evaluating carbon emissions, due to the lack of long-run data. To narrow this gap, an integrated framework combining the continuous input-output tables from 1997 to 2018 with the Mann-Kendall test and vector auto-regression was presented to clarify the fluctuating trend of household embedded carbon emissions and the driving pattern of low fertility, aging, and urbanization. Our main findings showed that changes in household embedded carbon emissions have increased sharply in the last two decades. The growth of Chinese household embedded carbon emissions began to accelerate in 2001, which lagged 1 year behind the demographic indicators. Low fertility has a positive impact on households' embedded carbon emissions. More importantly, the impact of low fertility is more significant and far-reaching than that of aging. These suggest that aggressive policies for stimulating fertility and low-carbon lifestyles should be considered by policy makers., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

There is a growing emphasis on fostering green growth and lowering carbon emissions in order to achieve sustainable economic development. This study uses the Tapio decoupling model and analyzes the factors influencing changes in carbon emissions from manufacturing in India utilizing the log mean Divisia index (LMDI) techniques. Furthermore, the nexus between carbon emission intensity, information and communication technology (ICT), total factor productivity (TFP), skill, and energy intensity has been analyzed using the system-GMM approach. It is based on the plant-level Annual Survey of Industries (ASI) datasets for the organized manufacturing sector of India from 2001 to 2002 to 2019 2020 for the major 21 Indian states/UT. The findings reflect the presence of weak decoupling in the manufacturing sector both at the aggregate level and in states. This indicates that both output and emissions are increasing; however, output growth surpasses emission growth, which signifies an effort to transition towards more environmentally friendly production methods and enhanced energy efficiency. The output and population effect are found to be leading factors in carbon emissions, while energy intensity is found to be reducing the effect. Further, the system-GMM estimates show that ICT and energy intensity positively affect total factor productivity, while with an increase in carbon emission intensity, productivity declines. The study confirms the existence of an inverted N-shaped Kuznets curve in the sector. This present study will contribute to formulating energy and environmental strategies to reduce emissions and promote adopting cleaner energy sources. These efforts will facilitate the attainment of carbon neutrality and enhance energy efficiency within the sector., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Road transportation is an important contributor to carbon emissions. China's car ownership is rapidly increasing, ranking first worldwide; however, there are limited data about carbon emission inventories. This study assesses carbon emissions from road transportation from the past to the future across China, using market survey, COPERT (Computer Programme to Calculate Emissions from Road Transport) model, and a combination method of principal component analysis and backpropagation neural network. From 2000 to 2020, the national carbon emissions from road transportation grew from 11.9 to 33.8 Mt CO 2 e, accounting for 0.47% of national total emissions by then. Trucks generally emit a higher proportion (77.3%) of total emissions than passenger cars (18.9%); however, the emission proportion of passenger cars (18.9-31.0%) has increased yearly. The carbon emissions at the prefecture level show an urban agglomeration trend, decreasing from the eastern coastal areas to central China. Future car ownership is expected to grow rapidly at 3.1% during 2021-2049, but only half of that growth rate during 2051-2060. Those vehicles are expected to contribute carbon emissions of 27.2-39.1 Mt CO 2 e under different scenarios in 2060. Scientifically reducing emissions and innovatively reducing the carbon emission coefficient, combined with a reasonable new energy vehicle growth scenario, are efficient methods for reducing national carbon levels. This study demonstrates that the uncertainty is within an acceptable range. This work details the carbon emission inventories associated with road transportation in China and provides basic data for developing a better carbon reduction policy for China's car industry., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Logistics and transportation industry is not only a major energy consumer, but also a major carbon emitter. Developing green logistics is the only way for the sustainable development of the logistics industry. One of the main factors of environmental pollution is caused by carbon emissions in the process of vehicle transportation, and carbon emissions of vehicle transportation are closely related to routing, road conditions, vehicle speed, and speed fluctuations. The low-carbon vehicle routing problem with high granularity time-dependent speeds, speed fluctuations, road conditions, and time windows is proposed and formally described. In order to finely evaluate the effects of vehicle speed and speed fluctuations on carbon emissions, a graph convolutional network (GCN) is used to predict the high granularity time-dependent traffic speeds. To solve this complicated low-carbon vehicle routing problem, a hybrid genetic algorithm with adaptive variable neighborhood search is proposed to obtain vehicle routing with low carbon emissions. Finally, this method is validated using a case study with the logistics and traffic data in Jingzhou, China, and also the results show the effectiveness of this proposed method., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Over the past two decades, the Taiwan government promulgated some regulatory measures and promotional actions on energy efficiency promotion and renewable energy development. In March 2022, the "Taiwan's Pathway to Net-Zero Emissions in 2050" was announced to respond to the Paris Agreement. In order to achieve the goal, the Climate Change Response Act (CCRA) was passed on February 15, 2023, requiring the de-carbonization measures and adaptation strategies. The main aim of this paper was to analyze the changes in GHG emissions and renewable energy supply by using the updated data from the official statistics in connection with the trends of environmental and energy sustainability since 2000. The findings showed that total installed capacity of renewable power (especially in solar power and wind power) showed an amazing increase over the past decade, leading to the inclined GHG emissions and thus supporting the environmental and energy sustainability toward a low-carbon society. Furthermore, this paper summarized the development history and main differences concerning the carbon neutrality policy and legislation in Japan and South Korea. For achieving the staged targets of GHG emissions by 2030 and 2050, this paper finally addressed the technological approaches for achieving carbon neutrality by 2050 in Taiwan, focusing on the transformation of energy and industry, and the policy implications by all levels of government., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper focuses on examining the effects of per capita environmental technology development on the load capacity factor (LCF) within the context of OECD member countries during the period spanning 1990 to 2021. To investigate these relationships, we employ the AMG estimator and FM-LS estimator. Additionally, we explore the validity of the load capacity curve for these countries and estimate the inflection points in the income per capita-environmental sustainability relationship. Our findings lead us to the conclusion that there is no significant impact of environmental technologies on environmental sustainability. Furthermore, we observe a negative influence on the development of environmental technologies, which can be attributed to the negative externalities associated with their implementation and the lack of societal adoption. Moreover, our estimations reveal an inflection point at $45,251.90 in terms of GDP per capita, beyond which the sustainability condition of the studied countries improves., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study assesses the influence of climate policy uncertainty (CPU) on carbon emissions (CE) against the backdrop of economic policy uncertainty (EPU) in the US. The wavelet analysis provides a comprehensive understanding of correlations in the time and frequency domains. The results demonstrate a significant correlation between CPU and CE, which varies across different time periods and frequencies. In the time domain, the results indicate that the CPU and CE move together during certain subperiods. Moreover, there are observable comovements in the frequency domain, particularly in the short to medium range. However, the correlation becomes stronger in the short term when there is no EPU, suggesting a closer interaction between CPU and CE. Therefore, it is crucial for governments to prioritize improving the clarity, credibility, and consistency of climate policies. They should also consider potential economic shocks when designing these policies., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Soil organic carbon (SOC) is a crucial medium of the global carbon cycle and is profoundly affected by multiple factors, such as climate and management practices. However, interactions between different SOC fractions and land-use change have remained largely unexplored in karst ecosystems with widespread rock outcrops. Owing to the inherent heterogeneity and divergent response of SOC to land-use change, soil samples with close depth were collected from four typical land-use types (cropland, grassland, shrubland, and forestland) in the karst rocky desertification area of China. The aim of this study was to explore the responses of SOC dynamics to land-use types and underlying mechanism. The results showed that land-use type significantly affected SOC contents and its fractions. Compared with cropland, the other three land uses increased the total organic carbon (TOC), microbial biomass carbon (MBC), and non-labile organic carbon (NLOC) contents by 6.11-129.44%, 32.58-173.73%, and 90.98-347.00%, respectively; this demonstrated that a decrease in both labile and recalcitrant carbon resulted in SOC depletion under agricultural land use. Readily oxidized organic carbon (ROC) ranged from 42 to 69%, accounting for almost half of the TOC in the 0-40-cm soil layer. Cropland soil showed significantly higher ROC:TOC ratios than other land-use types. These results indicated that long-term vegetation restoration decreased SOC activity and improved SOC stability. Greater levels of soil exchangeable calcium (ECa) and clay contents were likely responsible for higher stabilization and then accumulation of SOC after vegetation restoration. The carbon pool index (CPI) rather than the carbon pool management index (CPMI) exhibited consistent variation trend with soil TOC contents among land-use types. Thus, further study is needed to validate the CPMI in evaluating land use effects on soil quality in karst ecosystems. Our findings suggest that land-use patterns characterized by grass or forest could be an effective approach for SOC-sequestration potential and ensure the sustainable use of soil resources in the karst area., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Industrial land is currently the main carrier and important source of global carbon emissions, and as the world's largest developing country, China's large-scale and diversified industrial land supply has made it the world's largest carbon emitter. Therefore, researching the impact of different supply methods of industrial land on carbon emissions and its impact paths in China can help provide a reference for other countries to reduce carbon emissions from the perspective of urban industrial land management, which is of great significance for effectively promoting global carbon reduction. Based on this, this paper analyses the impact of different supply methods of industrial land on carbon emissions and its urban heterogeneity using the SYS-GMM and chain-mediated effects models for 285 cities in China from 2008 to 2020. The study found that, in general, the impact of different industrial land transfer modes on carbon emission has hysteresis and persistence. Agreement and listing transfer with government intervention can significantly exacerbate carbon emissions, while more market-based bidding and auction transfer can dampen carbon emissions. In terms of intermediary effects, the transfer of industrial land by agreement and listing will inhibit the rationalization and advancement of industrial structure, thus aggravating carbon emissions. The transfer of industrial land by bidding and auction will create barriers to entry and a crowding-out effect, promote the rationalization of industrial structure and the transformation and upgrading of industrial structure and moderate carbon emissions. In terms of city heterogeneity, there is urban heterogeneity in the impact of industrial land transfer on carbon emissions in cities with different economic types. Bidding and auction transfer for industrial land in both economically developed and less developed cities can promote carbon pollution. While the more developed urban economy makes the intermediary effect of industrial structure not significant. In the future, it is necessary to strictly control the scale of industrial land supply; the whole process supervision mechanism of industrial land allocation and differentiated industrial land supply strategies will provide useful experience for many developing countries in allocating industrial land to mitigate carbon emissions, generating effective contributions to global carbon emission reduction., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Mitigating carbon emissions from crop production is essential for addressing global warming. At a macro-level, existing studies have often relied on the calculation of carbon emission intensity of crop production to understand comparable carbon effects between regions. However, this approach obscures the differences in crop planting scale and natural attributes across regions, leaving room for improvement in the methods and scope of analysis. To extend the existing research, we proposed an idea for calculating the carbon emission density of crop production based on planting area. Additionally, we developed an analytical framework for driving factors of carbon emission density of crop production from a spatial interaction perspective. The provincial carbon emission density of crop production in mainland China between 2000 and 2020 was calculated, and spatial econometric models were utilized to investigate the spatial autocorrelation and driving factors. The results indicate that the national average carbon emission density of crop production was 1.462 t/hm 2 annually. Over 21 years, the carbon emission density of agricultural materials, rice cultivation, soil management, and straw burning evolved from 0.384 to 0.470 t/hm 2 , 0.409 to 0.367 t/hm 2 , 0.171 to 0.169 t/hm 2 , and 0.317 to 0.448 t/hm 2 , respectively. The global Moran's index indicated a positive spatial autocorrelation of carbon emission density of crop production and the subdivided carbon sources among provinces. Regarding direct effects, an increase in the proportion of paddy fields in cropland composition and irrigation efficiency would significantly promote the carbon emission density, while factors such as cropland area, multiple cropping, agricultural personnel numbers, departmental proportion, and disaster degree would decrease the local carbon emission density. Certain factors, such as cropland area and agricultural disasters, had a spatial spillover effect on carbon emission density between provinces. The study suggests harnessing key drivers and spatial spillover effects to achieve regional low-carbon crop production., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The explosive growth of the logistics industry has led to an increase in energy consumption and carbon emissions. To reduce emissions and increase the efficiency of the logistics industry, we studied the driving factors and decoupling effects of carbon emissions of logistics industry (LICE). First, an energy coefficient method is used to calculate the LICE. Second, the Logarithmic Mean Divisia Index (LMDI) decomposition method is used to decompose the driving factors of LICE into five types. Third, the decoupling model is used to explore the decoupling relationship between economic growth and LICE. Considering Anhui Province as an example, this study describes the method's implementation process based on a comparison of the four Yangtze River Delta provinces and cities. The results indicate that the growth rate of LICE in Anhui Province has decreased during the research period, from 9.7% in 2013 to 2.1% in 2021; however, the Tapio decoupling elasticity has been approximately 0.4 for the last 2 years, remaining in a weak decoupling stage from economic development. The LMDI decomposition results indicate that the average contribution of economic level to carbon emissions is 1.763. This study proposes some solutions and recommendations for the logistics industry's low-carbon development to offer methodological and theoretical support for LICE research., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The formulation of long-term step-by-step emission reduction plan is an important step for effective scientific emission reduction. This paper takes Shanghai as the research object, constructs PSO-LSTM model on the basis of STIRPAT model, and further constructs three dynamic policy scenarios combined with China's actual situation and makes short-, medium-, and long-term multivariate predictions for them. The study finds that only the improvement of energy consumption structure has a promotion effect on carbon emission reduction, and urbanization, industrial structure, technology level, population, and economic level all have an increasing effect, and secondly, the carbon emission reduction path of Shanghai basically achieves the core objective of steady decrease under the three scenarios. Secondly, under the three scenarios, Shanghai's carbon emission reduction path basically achieves the core objective of steady decline, but the decline in the GU scenario is more significant. It is recommended that Shanghai further adjusts its industrial structure, optimizes its energy consumption structure, promotes technological innovation and progress, and promotes the development of the circular economy model., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Urban agglomerations are the centers of carbon emissions. However, research on sector-specific carbon emissions in different urban agglomerations is still limited. Drawing on the data of China's six urban agglomerations in 2005, 2010, and 2015, this study investigates the spatio-temporal patterns, regional inequalities, and driving forces of total, industrial, transportation, and residential carbon emissions. The study found that Beijing-Tianjin-Hebei was the total and sectoral emission center among the studied urban agglomerations. Additionally, regional carbon inequalities gradually decreased, implying a growing regional synergistic carbon pattern. The driving forces of carbon emissions, including population, GDP, energy intensity, secondary industry, tertiary industry, foreign investment, urbanization, and green coverage, varied across sectors and regions. Notably, foreign investment could lead to lower carbon emissions in less developed agglomerations like Beijing-Tianjin-Hebei, the Central Plains, and the middle reaches of the Yangtze River, whereas more developed agglomerations like the Yangtze River Delta and the Pearl River Delta benefited less from foreign investment. Besides, ChengYu has good ecological conditions and sustainable development modes, which linked urbanization and green space to reduced carbon emissions in the industrial sector. The findings can help formulate differentiated carbon policy and support sustainable development., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Climate change poses an urgent threat, necessitating the implementation of measures to actively reduce carbon emissions. The development of effective carbon emission reduction policies requires accurate estimation of the costs involved. In situations where actual prices of commodities are not available in the market, shadow pricing provides a useful method to calculate relative prices between commodities with and without price information. However, most studies focus on the industry, with few contributions on agricultural sector. This paper estimates the shadow price of carbon emissions in the agricultural sector from a provincial perspective, incorporating the impact of livestock into the calculation of carbon emissions and shadow pricing. Our findings indicate that ignoring livestock may overestimate CSP values. On the whole, the level of carbon shadow price is rising, indicating good green development in China's agricultural sector. The two types of convergence results show that there is sigma convergence and beta convergence in the western and central regions, demonstrating a significant improvement in environmental performance., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Technological innovation is regarded as an important means to improve carbon efficiency. However, there is no consensus on this view. Meanwhile, few studies have considered how technological innovation affects carbon efficiency. To this end, this study investigates the influencing mechanism and effects of technological innovation on carbon emission efficiency from the perspectives of industrial restructuring and R&D element flow. It establishes the influencing and mechanism model and then deeply studies the impact and paths of technological innovation on carbon emission efficiency, using panel data from 30 provinces in 1999-2020. Results show that (1) technological innovation improves carbon emission efficiency. (2) Regional differences in the impact effects of technological innovation are evident, with a greater contribution to carbon emission efficiency in eastern region. (3) Innovation improves carbon efficiency through two paths: advanced industrial structure and industrial structure rationalization. (4) The moderating effect demonstrates that the technological innovation's influence is gradually enhanced with the interregional mobility of R&D personnel and capital. Hence, decision-makers should correctly guide the orderly flow of R&D factors and further improve the carbon emission reduction effect by increasing innovation support and helping optimize the industrial structure., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Improving green water use efficiency is based on the synergy of reducing pollutants and carbon emissions, and effectively identifying its spatial development structure is a prerequisite for realizing the green transformation of water management. This paper adopted the slacks-based measure- "energy-environment-economy" (SBM-3E) model with carbon emission and pollution indicators as unanticipated outputs to measure the green water use efficiency of the 11 provinces (cities) in the Yangtze River Economic Belt from 2000 to 2018, then analyzed the efficiency network structure evolution by using the social network analysis method, and finally examined the drivers of efficiency networks. The results show that (1) the value of green water utilization efficiency decreases from 0.78 to 0.51, and its spatial distribution is downstream > middlestream > upstream, with the values of 0.41, 0.61, and 0.86, respectively. (2) The overall spatial network efficiency is gradually interconnected, with the density increasing from 0.32 to 0.6; it has undergone a structural evolution from "flat" to "inverted tower." The core-edge structure of the main body status within the basin has been strengthened. (3) The efficiency network is significantly influenced by water resource endowment, industrial structure, and foreign investment level. The conclusions are expected to provide useful insights for designing the basin's water protection policy and greening the region's development., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

A comprehensive understanding of the terrestrial carbon sink is essential for proficient regional carbon management. However, previous studies predominantly relied on net ecosystem productivity (NEP) as an indicator of regional carbon sink, overlooking the impacts of carbon emissions from physical processes and carbon leakage associated with anthropogenic activities. In this study, net region productivity (NRP), a vital metric representing carbon sink dynamics in regional multi-landscape ecosystems, was employed to systematically analyze the patterns, trends, and causes of carbon sink in Ordos. The results revealed that spatially averaged NRP in Ordos was 70.334 g·m -2 ·a -1 , indicating a carbon sink effect. The coefficient of variation of NRP was 68.035%, with a higher NRP in the southern region. Normalized difference vegetation index (NDVI) predominantly controlled the spatial heterogeneity of NRP in Ordos, while precipitation emerged as the primary climatic factor influencing spatial differences in NRP. Regional variations in the impact of environmental factors on NRP were evident. In most areas, NRP showed a notable increasing trend influenced by various factors. Specifically, the simultaneous rise in NDVI and improvements in hydrothermal conditions contributed to the gradual elevation of NRP, each with varying degrees of influence across Ordos and its sub-regions., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In recent years, there has been a persistent intensification of the global greenhouse effect. Balancing carbon emission reduction with economic growth poses an unprecedented global challenge. To better comprehend the relationship between economic growth and carbon emissions, this study first utilized the Tapio decoupling index to compare the decoupling relationship (the USA, Japan, and Germany) and three developing countries (China, India, and Russia) from 2000-2020. Additionally, the logarithmic mean Divisia index (LMDI) method was employed to investigate the factors influencing changes in carbon emissions. Our findings indicate that (1) the USA and Germany basically achieved strong decoupling; China, India, and Russia mainly showed weak decoupling; and Japan showed recessive decoupling. (2) Economic growth predominantly contributed to increased carbon emissions, with a lesser impact from population growth. A significant reduction in energy intensity restrained carbon emissions growth, as did energy structure replacement in most countries, excluding Japan. Based on this, a decoupling effort index was formulated. It has shown that the decoupling efforts made by developing countries are weaker than those of developed countries, primarily attributed to a lesser degree of decoupling between energy intensity and structure. This paper offers valuable insights for developing countries undergoing a low-carbon economic transformation. They should counterbalance carbon emission escalation resulting from economic growth through technological and energy structure improvements., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The purpose of this study is to examine the impact of gross domestic product, energy consumption, and trade openness on carbon emission in Asia. Among the 48 countries in Asia, 42 were included in the analysis, spanning a period of 20 years. Given that Asia is the predominant contributor, accounting for 53% of global emissions as of 2019, a comprehensive examination at both continental and individual country levels becomes imperative. Such an approach aligns with local, regional, and global development agendas, contributing directly and indirectly to climate change mitigation. The analytical techniques employed in this study encompassed panel regression and multiple linear regression, illuminating the specific contributions of each country to the study variables and their impact on carbon emissions. The findings suggest that gross domestic product (13 out of 42 countries), energy consumption (21 out of 42 countries), and trade openness (eight out of 42 countries) have a highly significant impact (p < 0.01) on carbon emissions in Asia. Energy consumption plays a vital role in increasing carbon emissions in Asia, driven by rising populations, urbanisation, and oil and gas production. Policymakers can take several actions such as adopting a carbon pricing system, using sustainable transportation, renewable energy development, and international cooperation within Asia to reach the goal of being carbon neutral by 2050., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Ex-mining lake-converted constructed wetlands play a significant role in the carbon cycle, offering a great potential to sequester carbon and mitigate climate change and global warming. Investigating the quantity of carbon storage capacity of ex-mining lake-converted constructed wetlands provides information and justification for restoration and conservation efforts. The present study aims to quantify the carbon pool of the ex-mining lake-converted constructed wetlands and characterise the physicochemical properties of the soil and sediment. Pearson's correlation and a one-way ANOVA were performed to compare the different sampling stations at Paya Indah Wetland, Selangor, Malaysia. An analysis of 23 years of ex-mining lake-converted constructed wetlands of Paya Indah Wetlands, Selangor, Malaysia, revealed that the estimated total carbon pool in soil and sediment accumulated to 1553.11 Mg C ha -1 (equivalent to 5700 Mg CO 2 ha -1 ), which translates to an annual carbon sink capacity of around 67.5 Mg C ha -1 year -1 . The characterisation showed that the texture of all soil samples was dominated by silt, whereas sediments exhibited texture heterogeneity. Although the pH of the soil and sediment was both acidic, the bulk density was still optimal for plant growth and did not affect root growth. FT-IR and WDXRF results supported that besides the accumulation and degradation of organic substances, which increase the soil and sediment carbon content, mineral carbonation is a mechanism by which soil and sediment can store carbon. Therefore, this study indicates that the ex-mining lake-converted constructed wetlands of Paya Indah Wetlands, Selangor, Malaysia have a significant carbon storage potential., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Amid China's economic shift to high-quality development, addressing environmental challenges like greenhouse gas emissions and manufacturing pollution, there is a crucial demand for sustainable and eco-friendly development strategies. This study aims to investigate the impact of innovation efficiency in the high-tech industry on carbon emissions. It seeks to explore regional differences, mechanisms, and the influence of energy consumption structures in achieving sustainable development goals. Utilizing data from 30 provinces spanning 2009 to 2020, the study employs the DEA-Malmquist index model, spatial and temporal classification evaluation, and a panel measurement model to assess the efficiency of innovation and development in high-tech industries and their relationship with carbon emissions. The results indicate several key findings: (1) The overall operational efficiency of high-tech industry innovation and development in China is steadily increasing. However, there are distinct characteristics observed among provinces and cities, reflecting diverse input and output types. (2) High-tech industry innovation efficiency significantly contributes to carbon emission reduction, and there is regional heterogeneity in this impact. The central and western regions exhibit greater effects compared to other provinces and cities. (3) The optimization of the energy structure is identified as a mechanism through which high-tech industry innovation efficiency reduces carbon emissions. Moreover, different intervals of high-tech industry innovation efficiency yield varying effects on carbon emissions. This research underscores the importance of fostering high-tech industry innovation efficiency as a means to reduce carbon emissions. It also identifies key areas for future policy development and resource allocation, emphasizing the support needed for low-carbon technology research and development., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This paper introduces a novel methodology for estimating carbon total factor productivity based on the Cobb-Douglas production function. The research method introduced in this article expands upon the framework for assessing total factor productivity through the Solow residual method. It unifies the conceptual and methodological aspects of carbon total factor productivity with those of single factor productivity. Utilizing panel data from various provinces between 2010 and 2021, we computed carbon total factor productivity to understand its implications for China in combating global climate change. We demonstrate that (i) we have introduced a method to handle negative coefficients in the Cobb-Douglas production function by incorporating productivity in lieu of input factors during calculations. (ii) Carbon total factor productivity, encapsulating the geometric weighted mean of labor, capital, and carbon productivity, holds notable economic relevance. Further, it serves as an integrative metric comprising carbon productivity intertwined with the mean labor and capital carbon factors. And (iii) the influence of carbon total factor productivity growth on economic progression remains relatively subdued, with escalating labor force growth posing detrimental effects on several provincial economies. Enhancing carbon total factor productivity emerges as an imperative to harmonize robust economic growth with strategic carbon curtailment. Our analytical framework provides nuanced perspectives on productivity determinants, accentuating the thrust towards sustainable evolution amidst climatic challenges. This investigation bears profound significance for policymakers endeavoring to sculpt a carbon-conscious economic paradigm in consonance with global climatic ambitions., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The low-carbon city (LCC) pilot policy is significant in tackling carbon emission reduction and environmental protection. Several studies have demonstrated that LCC policy dramatically decreases air pollution in cities for environmental preservation, while there is a shortage of research on banking development. Using city-level data from 2005 to 2018 in China, we examine the impact of the LCC pilot policy on the growth of the banking sector by using a difference-in-difference (DID) model and analyze the underlying transmission mechanism. The empirical result demonstrates that the construction of LCC significantly impedes the growth of the regional banking sector, and the conclusions above hold up to a battery of tests. First, the LCC policy drastically reduces the scale of industries in the pilot cities, including secondary industry output, tertiary industry output, fixed asset investment, and fixed asset balance. Second, the LCC policy encourages industrial reorganization in the pilot cities, as demonstrated by a change in market demand from high carbon-emitting enterprises and non-state-owned enterprises to low carbon-emitting enterprises and state-owned enterprises, respectively. The results indicate that the LCC policy produces credit risk at enterprises and conveys it to banks, resulting in a retarding effect on banking development., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The driving factors of China's industrial carbon emissions are decomposed by generalized Divisia index method (GDIM), so as to study the reasons for the change of China's industrial carbon emissions. The decoupling effect of China's industrial carbon emissions and economic growth is examined by speed decoupling and quantity decoupling. The speed decoupling is calculated by Tapio decoupling elasticity and emission reduction effort function, and the quantity decoupling is measured by environmental Kuznets curve (EKC). The results show that the positive driving factors are output size effect > industrial energy consumption effect > population size effect, and the negative driving factors are investment carbon emission effect > output carbon intensity effect > per capita output effect > economic efficiency effect > energy intensity effect. The elasticity of emission reduction is basically greater than that of energy conservation, indicating that there is still abundant room for efforts in emission reduction. The overall decoupling effect of carbon emissions is undecoupling-strong decoupling-undecoupling. Quadratic EKC shape is "U" shape, and the inflection point is 11.0987; the shape of cubic EKC is "N," and the inflection points are - 0.0137 and 2.4069, respectively, which satisfies the hypothesis of EKC curve., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Few studies provide direct evidences that agricultural fiscal affects agricultural carbon intensity. This study tries to fill this gap. Using panel data of 30 provinces in China from 2005 to 2019, we conclude that agricultural fiscal expenditures significantly reduce agricultural carbon intensity. The result is still robust after employing the provincial agricultural leaders' birthplace information as an instrumental variable. Further study shows that the negative effect of agricultural fiscal expenditures on agricultural carbon intensity is more pronounced in regions with less corruption and is also more visible in central, western, and inland regions than other areas. For this effect, agricultural technological improvement and structure optimization are possible channels, but not operation scale expansion. Interestingly, although agricultural fiscal expenditures reduce the local agricultural carbon intensity, neighbor regions' carbon intensities are increased due to fiscal rivalry., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The carbon mitigation response encompasses a variety of strategies aimed at mitigating greenhouse gas emissions resulting from human activities. These measures are crafted to address the challenges posed by climate change and facilitate the transition of businesses towards a low-carbon paradigm. Leveraging the analytical outcomes of the extended STIRPAT model and the PSO-BP prediction model, this paper suggests countermeasures for reducing carbon emissions in China's metal smelting industry. The overarching objective is to contribute to China's attainment of the "dual carbon objectives." The study identifies key factors influencing carbon emissions in the metal smelting industry, ranked in descending order of sensitivity: population, coal consumption, urbanization rate, total metal production, carbon intensity, proportion of secondary industry, and GDP per capita. Results from three established scenarios-namely, low carbon, standard, and high carbon-indicate a consistent decline in carbon emissions from China's metal smelting industry over the next 15 years. This research not only enhances the findings of existing studies on carbon emissions in the metal smelting sector but also introduces an innovative approach to carbon emission reduction within China's metal smelting industry., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Resource-based cities are important strategic bases for securing resources in China and have made great contributions to the country's economic development. Long-term extensive resource development has made resource-based cities an important region constraining China from achieving comprehensive low-carbon development. Therefore, it is of great significance to explore the low-carbon transition path of resource-based cities for their energy greening, industrial transformation, and high-quality economic development. This study compiled the CO 2 emission inventory of resource-based cities in China from 2005 to 2017, explored the contribution to CO 2 emissions from three perspectives (driver, industry, and city), and predicted the peak of CO 2 emissions in resource-based cities. The results show that resource-based cities contribute 18.4% of the country's GDP and emit 44.4% of the country's CO 2 and that economic growth and CO 2 emissions have not yet been decoupled. The per capita CO 2 emissions and emission intensity of resource-based cities are 1.8 times and 2.4 times higher than the national average, respectively. Economic growth and energy intensity are the biggest drivers and main inhibitors of CO 2 emissions growth. Industrial restructuring has become the biggest inhibitor of CO 2 emissions growth. Based on the different resource endowments, industrial structures, and socio-economic development levels of resource-based cities, we propose differentiated low-carbon transition pathways. This study can provide references for cities to develop differentiated low-carbon development paths under the "double carbon" target., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The low-carbon transition is a systemic economic and social change that will inevitably have an impact on many areas of the urban system. Has China's ongoing low-carbon transition impacted urban resilience (UR) systems while achieving urban energy saving and carbon emission reduction goals? This paper uses the implementation of the carbon emissions trading pilot policy (CETPP) as a "quasi-natural experiment." It evaluates the impact of the policy on UR using a difference-in-differences model based on the data of prefecture-level cities from 2008 to 2020. The study shows that pilot carbon trading policies favor UR, and the market mechanism of carbon emissions has a heterogeneous cause influence on UR. The impact of pilot carbon trading policies on UR varies according to the respective moderating effects of institutional factors, green technology innovation, industrial structure rationalization, and output effects., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Land use and land cover (LULC) will cause large flows of carbon sources and sinks. As the world's largest carbon emitter with a complicated LULC, China's carbon emissions have profound implications for its ecological environment and future development. In this paper, we account for the land-use changes and carbon emissions of 30 Chinese provinces and cities in China from 2000 to 2020. Furthermore, the spatial correlation of carbon emissions among the study areas is explored. Four typical regions with spatial association (Beijing, Hebei, Sichuan, and Anhui) are selected, and their land-use change trends in 2025 and 2030 are simulated to predict the total carbon emissions in the future. The results show that the distribution of land-use in China is mainly cultivated and woodland, but the growth of urban built-up (UBL) land area indirectly leads to the continuous increase of carbon emissions. Total carbon emissions have increased over the past two decades, albeit at a slower growth rate, with some provinces experiencing no further growth. In the typical regional carbon emission simulation, it is found that the carbon emissions of the four provinces would show a downward trend in the future. The main reason is the reduction in indirect carbon emissions from fossil energy in UBL, while the other part is the influx of carbon sinks due to grassland, woodland, etc. We recommended that future carbon reduction measures should focus and prioritize controlling fossil energy and mitigating carbon emissions from UBL. Simultaneously, the significant contribution of forests and other land types as carbon sinks should be acknowledged to better implement China's carbon neutral commitment., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Globally, the transportation industry has become one of the leading sectors in carbon emission, and all countries are committed to environmental protection and energy conservation while experiencing rapid development. Under China's "dual-carbon" goal, the carbon emission problem hinders the construction of China's green transportation system and affects the high-quality development of transportation, so it is of great significance to study the spatial pattern of carbon emission efficiency in the transportation industry and the factors affecting it. Firstly, this paper measures the carbon emission value of transportation in 30 provinces in China from 2010 to 2020 based on the IPCC method and measures the carbon emission efficiency through the super-efficiency slack-based measurement model. Secondly, spatial autocorrelation analysis was conducted to determine the spatial clustering characteristics of the efficiency values. Finally, two spatial Durbin models are constructed to measure the spatial spillover effects and analyze the short-term immediate effects of each influencing factor on the static model and the long-term effects of the dynamic model considering the time lag of the transportation carbon emission efficiency. The results of the study show that (1) the average value of efficiency in the central and eastern regions is basically higher than 0.5; in the western and northeastern regions, it is basically lower than 0.3.The overall efficiency of carbon emission in the region shows a fluctuating upward trend but with increasing regional differences. (2) The number of regions with positive spatial correlation increased from 21 to 25 during the study period, and the degree of provincial transportation carbon emission efficiency agglomeration increased. (3) Although urbanization and energy intensity have a large detrimental influence on transportation carbon emission efficiency, environmental regulation has a major favorable effects on it both long and short term. Population scale, opening level, and urbanization all have significant spatial spillover effects. Accordingly, relevant policy recommendations are put forward to provide theoretical guidance for promoting the realization of low-carbon transportation., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study develops a novel Taguchi-STIRPAT input-output (TSIO) model for exploring pathways to reduce carbon emission from the perspective of household consumption, through incorporating input-output model (IOM), Taguchi design (TD), and STIRPAT model. TSIO can not only identify the main factors (carbon emission intensity, consumption structure, per capita consumption, and population) and evaluate their effects on indirect household carbon emissions (IHC), but also predict IHC from a long-term perspective to achieve the dual-carbon target. TSIO is then applied to Fujian province (China), where multiple scenarios related to multiple factors with multiple levels are examined. Results reveal that (i) among all sectors, the highest contributors to IHC are residence (RES), followed by food, cigarettes, and drinks (FCD), and transport and communication (TC); it is suggested that the government can consider market mechanism to control these high-carbon emission consumption behaviors; (ii) the decline in the share of RES consumption has the largest effect on rural and urban IHC; the share of RES consumption is considered to be a key factor in predicting carbon emissions; (iii) under the optimal scenario, IHC would peak in 2025 and decrease to 10.07 × 10 6 ton in 2060; this scenario can effectively mitigate household carbon emissions by reducing carbon emission intensity and the share of RES consumption; at the same time, it can ensure a sustained increase in per capita consumption. The results unveil the pathways of household carbon reduction under the dual-carbon target in Fujian province and suggest the local government should adopt policies (such as taxation and financial incentives) to limit residential consumptions with high carbon emission intensity., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

China's energy-intensive industries utilize the leading proportion of coal to meet the demand for its industrial outputs, while on the other hand, these industries also assure the provision of livelihood to millions of people, and capping the share of coal consumption for these industries can adversely affect the industrial and economic growth of China. Thus, to achieve the Pareto improvement between environmental pollution and industrial output growth, it is essential to comprehend the patterns of coal consumption in these industries. Hence, the present research intended to analyze the potential drivers of coal consumption by applying a joint LMDI, DEA, and the production theoretical decomposition approach. Findings indices that, first, industrial output growth was the crucial driver to simulate the industrial coal consumption, while the potential coal intensity and coal technology changes exhibited the reverse effect. Second, the coal inputs and industrial output efficiency, along with the improvements in technological gaps, were found to be the imperative factors in decelerating coal consumption. Third, the energy industrial group was discovered to have more potentials of coal conversation as compared to the non-energy industrial group. Moreover, results also indicated that coal pure technical efficiency is accelerating coal growth, which revealed that coal can be saved by enhancing coal allocative efficiency. These findings laid the empirical ground to design a feasible coal conservation policy for achieving the imperative goals of environmental protection without compromising industrial output growth., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The Beijing-Tianjin-Hebei region is not only an important economic center in China, but also one of the major regions contributing to China's carbon emissions. Revealing the spatial distribution between carbon emissions and economic growth is essential for the formulation of low-carbon development policies. Following the principle from macro to micro, this paper investigates the spatial evolution trend and distribution characteristics between carbon emissions and economic growth in the Beijing-Tianjin-Hebei region from 2005 to 2020 by applying imbalance index model, the rank-scale rule, and decoupling index model. The results show that the imbalance index of carbon emissions decreased between 0.0601 and 0.0533 in a fluctuating way, indicating that the imbalance of spatial distribution of carbon emissions decreases. The imbalance index of economic growth increased between 0.0738 and 0.0851, indicating that economic growth has become more disequilibrated, and the spatial evolution of carbon emissions is not coordinated with economic growth. The Zipf dimension of carbon emissions declined from 1.1806 in 2005 to 0.9594 in 2020, and carbon emissions declined in big cities and increased in cities of the middle order. The Zipf dimension of economic growth increased from 1.1384 in 2005 to 1.2388 in 2020, and the economic growth monopoly in big cities increased. The decoupling coefficient of carbon emissions to economic growth declined, and the driving effect of economic growth on carbon emissions diminished. It is recommended that the Beijing-Tianjin-Hebei region should coordinate the allocation of factors and coordinate industrial adjustment. Hebei should accelerate industrial upgrading and establish a diversified industrial system., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The total change in carbon emissions in the Bohai Rim Region (BRR) plays a guiding role in the policy formulation of carbon emission reduction in northern China. Taking the 43 cities in the BRR as an example, the spatial-temporal evolution of carbon emissions in the BRR was analyzed using kernel density estimation (KDE), map visualization, and standard deviation ellipses, and the spatial autocorrelation model was used to explore the spatial clustering of carbon emissions. On this basis, the spatial-temporal heterogeneity of the factors influencing carbon emissions is explained using a Geodetector. The results are as follows: (i) During the study period, the carbon emissions in the BRR were on the rise, the share of carbon emissions in the Beijing-Tianjin-Hebei region (BTHR) and Liaoning Province was decreasing, and the contribution of Shandong Province was gradually enhanced. The spatial distribution of carbon emissions shows a geographical pattern of "middle-high and low-outside." (ii) Carbon emissions from different regions show the characteristics of BTHR > Shandong Province > Liaoning Province. The high-value carbon emission area continues to move from the northwest of Beijing-Tianjin-Hebei to the southeast. (iii) Municipal carbon emissions showed a significant positive spatial correlation in the later part of the study. The high-high aggregation area is in Tianjin, and the low-low aggregation area is in Liaoning Province. (iv) The level of transport development contributes to carbon emissions with the highest growth rate, followed by industrial structure. There are also regional differences in the dominant influences on municipal carbon emission differences. Population size, urbanization, and economic development level are the core influencing factors of carbon emissions in the BTHR, Shandong Province, and Liaoning Province, respectively. In addition, the explanatory power of the interaction between the level of economic development and other factors on carbon emissions is at a high level., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)