Capita Carbon Emissions Research Articles

Reevaluating fair carbon emissions for households in Japan: basic energy needs and subsistence CO2 emissions

ABSTRACT Substantial inequalities in per capita carbon emissions have recently been highlighted. Given the diversity of people’s energy needs and extant differences in access to low-carbon energy, a fair emissions benchmark would not be equal CO2 emissions for all, but the CO2 emissions needed to enjoy a decent life ('subsistence emissions’). This study investigates the relationship between people’s basic energy needs and associated CO2 emissions, and illustrates the distribution of subsistence emissions among households in Japan, employing a unique and high-visibility technique. It finds that approximately 20% of households cannot meet their basic energy needs with one tonne of CO2 emissions per capita, and that an additional 1.56 million t-CO2 is required for all households to be able to meet their basic energy needs; this additional amount needs to be offset by further emissions reduction by households that consume a lot of domestic energy ('energy extravagant’) or non-residential sectors. With an equal-per-capita distribution of CO2 emissions in line with Japan’s 2030 target, approximately two-thirds of households would not be able to meet their basic energy needs, given the current energy mix and systems. In this context, low-carbon energy and technologies must be made more accessible for vulnerable populations to achieve both decarbonization and satisfaction of basic needs.

Unveiling the nonlinear drivers of urban land resources on carbon emissions: The mediating role of industrial upgrading and technological innovation

As cities continue to develop, land resources play a pivotal role in the pursuit of sustainable urban development. However, the impact and mechanisms of land resources on reducing emissions remain unclear. Therefore, our study employs an ensemble learning model within a Monte Carlo Simulation framework to explore the nonlinear relationship between land resources and carbon emissions. Additionally, we utilize a fixed effects model to identify the mechanisms of impact. Specifically, industrial and residential land uses are identified as primary drivers of carbon emissions, contributing 17.04 % and 4.75 % of the total variance in emissions, respectively. In contrast, a standard deviation increase in land price leads to a reduction of 4.65 tons in per capita carbon emissions. In addition, industrial upgrading and innovation emerge as critical mediators in promoting carbon reduction. This research contributes to sustainable urban planning and policy-making, offering insights into how strategic land use planning reduces urban carbon footprints.

Emissions Reduction Effects and Carbon Leakage Risks of Carbon Emissions Trading Policy: An Empirical Study Based on the Spatial Durbin Model

China’s carbon emissions trading policy represents a significant institutional innovation designed to advance the country’s economic and social development towards sustainability and low-carbon growth. This study investigates the effects of China’s carbon emissions trading policy by employing the difference-in-differences model and spatial Durbin model, using provincial panel data spanning from 2005 to 2020. We find that the carbon emissions trading policy can inhibit per capita carbon emissions in the pilot areas. This work is primarily driven by green technological innovation and the upgrade of industrial structure. Furthermore, the carbon emissions trading policy exhibits a positive spatial spillover effect, inhibits per capita carbon emissions in the areas adjacent to the pilot through demonstration effect and competition effect, and does not cause carbon leakage. These findings reveal the policy’s effectiveness in emissions reduction, and may be useful reference for promoting sustainable economic and social development. This is of great practical significance for exploring how to optimize environmental governance measures, avoid carbon leakage, and achieve balance and fairness in responsibilities in achieving low-carbon sustainable development. Our study proposes policy recommendations for synergizing the national trading market in China.

Cross-country distribution dynamics of carbon emissions and intensity: Before and after the global financial crisis

Our study employs the distribution dynamics approach to uncover transition probabilities and the long-term evolution of relative per capita carbon emissions (REPC) and relative carbon intensity (REPGDP) across 204 countries. We split the period of analysis into pre-crisis (2000-2007), and post-crisis (2007-2016) and divided countries into four income groups. The results indicate the emergence of new convergence clubs post-crisis in both REPC and REPGDP. On the one hand, the majority (many) of the low- (high) income countries congregate to extremely low (above the global average) REPC levels in the long run. On the other hand, most of the upper-medium- (high) income economies cluster around REPGDP levels far above (below) the global average. Finally, using mobility probability plots, we identify above-average carbon emitters with the highest probabilities of diverging further above the global average in the coming years. The study delivers nascent evidence supporting the usefulness of the MPP display tool in clarifying the positions and responsibilities of specific countries in future agreements on climate change. The results support the argument for using both measures of carbon emissions as a suite of future multilateral climate negotiations and policies.

MMD-TSC: An Adaptive Multi-Objective Traffic Signal Control for Energy Saving with Traffic Efficiency

Reducing traffic energy consumption is crucial for smart cities, and vehicle carbon emissions are a key energy indicator. Traffic signal control (TSC) is a useful method because it can affect the energy consumption of vehicles on the road by controlling the stop-and-go of vehicles at traffic intersections. However, setting traffic signals to reduce energy consumption will affect traffic efficiency and this is not in line with traffic management objectives. Current studies adopt multi-objective optimization methods with high traffic efficiency and low carbon emissions to solve this problem. However, most methods use static weights, which cannot adapt to complex and dynamic traffic states, resulting in non-optimal performance. Current energy indicators for urban transportation often fail to consider passenger fairness. This fairness is significant because the purpose of urban transportation is to serve people’s mobility needs not vehicles. Therefore, this paper proposes Multi-objective Adaptive Meta-DQN TSC (MMD-TSC), which introduces a dynamic weight adaptation mechanism to simultaneously optimize traffic efficiency and energy saving, and incorporates the per capita carbon emissions as the energy indicator. Firstly, this paper integrates traffic state data such as vehicle positions, velocities, vehicle types, and the number of passengers and incorporates fairness into the energy indicators, using per capita carbon emissions as the target for reducing energy consumption. Then, it proposes MMD-TSC with dynamic weights between energy consumption and traffic efficiency as reward functions. The MMD-TSC model includes two agents, the TSC agent and the weight agent, which are responsible for traffic signal adjustment and weight calculation, respectively. The weights are calculated by a function of traffic states. Finally, the paper describes the design of the MMD-TSC model learning algorithm and uses a SUMO (Simulation of Urban Mobility) v.1.20.0 for traffic simulation. The results show that in non-highly congested traffic states, the MMD-TSC model has higher traffic efficiency and lower energy consumption compared to static multi-objective TSC models and single-objective TSC models, and can adaptively achieve traffic management objectives. Compared with using vehicle average carbon emissions as the energy consumption indicator, using per capita carbon emissions achieves Pareto improvements in traffic efficiency and energy consumption indicators. The energy utilization efficiency of the MMD-TSC model is improved by 35% compared to the fixed-time TSC.

Spatio-Temporal Diversification of per Capita Carbon Emissions in China: 2000–2020

Exploring the low-carbon transition in China can offer profound guidance for governments to develop relevant environmental policies and regulations within the context of the 2060 carbon neutrality target. Previous studies have extensively explored the promotion of low-carbon development in China, yet no studies have completely explained the mechanisms of the low-carbon transition in China from the perspective of per capita carbon emissions (PCEs). Based on the statistics and carbon emissions data of 367 prefecture level cities in China from 2000 to 2020, this study employed markov chain, kernel density analysis, hotspots analysis, and spatial regression models to reveal the spatiotemporal distribution patterns, future trends, and driving factors of PCEs in China. The results showed that China’s PCEs in 2000, 2010, and 2020 were 0.72 ton/persons, 1.72 ton/persons, and 1.91 ton/persons, respectively, exhibiting a continuous upward trend, with evident regional heterogeneity. PCEs in northern China and the eastern coastal region were higher than those of southern China and the central and southwestern regions. The PCEs in China showed obvious spatial clustering, with hot spots mainly concentrated in Inner Mongolia and Xinjiang, while cold spots were mainly in some provinces in southern China. The transition of PCEs in China exhibited a strong stability and a ‘club convergence’ phenomenon. A regression analysis revealed that the urbanization level and latitude had negative effects on PCEs, while the regional economic development level, average elevation, average slope, and longitude showed positive effects on PCEs. These findings have important implications for the promotion of the low-carbon transition and the effective achievement of the “dual carbon” goal.

Global insights on the impact of digital infrastructure on carbon emissions: A multidimensional analysis

With the swift expansion of global digital infrastructure, understanding its impact on carbon emissions is essential for addressing global warming. This study develops a digital infrastructure indicator system. We use panel data from 83 countries spanning 2005 to 2021 to thoroughly examine the effects and underlying mechanisms of digital infrastructure on carbon emissions. Our findings indicate that digital infrastructure contributes to an increase in carbon emissions worldwide. Mechanism tests suggest that this increase is facilitated by capital agglomeration and fossil energy consumption. However, the impact of digital infrastructure on carbon emissions shows regional variations. In the Arab region, digital infrastructure development seems to reduce carbon emissions, a trend also observed, albeit potentially, in the CIS, Africa, and the Americas. In contrast, Europe and the Asia-Pacific experience a significant surge in carbon emissions due to digital infrastructure. Population density and the proportion of renewable energy emerge as critical threshold variables. Beyond a certain population density, the impact on carbon emissions intensifies, whereas an increase in renewable energy share beyond a specific point mitigates this effect. Robustness tests confirm that digital infrastructure elevates both per capita carbon emissions and carbon intensity, with digital markets and technologies notably amplifying carbon emissions.

Impact of newly extended metro line opening on commuting carbon emissions: A comparative study before and during the pandemic

Urban transportation is a key contributor to greenhouse gas emissions. Given that metro systems serve as prominent modes of public transportation, it becomes imperative to investigate the alteration in carbon emissions subsequent to the inauguration of new metro lines, along with the determinants influencing the metro commuting choices. Previous research tends to use surveys to collect individual travel data, which may have sample selection and self-reported data problems. This research combines big data and survey method to study commuting carbon emission change and influencing factors of metro adoption. Because COVID-19 also has an impact on individual commuting mode, this research further explores total and per capita carbon emissions before and during the pandemic period. Selecting Shenzhen metro lines as a study case, it is found that the opening of newly extended metro lines leads to an increased proportion of residents commuting by metro and a decreased proportion of car users, resulting in a reduction of commuting carbon emissions. Additionally, during the pandemic, people tended to choose private cars for commuting due to its advantage of independence and privacy, resulting in an increase in commuting carbon emissions. Finally, regarding influencing factors of metro adoption, it is also found that young and middle-aged, highly educated, low-income residents or residents without cars are more likely to choose metro as their commuting mode. This research suggests the local government promote public transit after the pandemic period and tailor transport policies for the various resident groups, including providing subsidies.

Response of Low Carbon Level to Transportation Efficiency in Megacities: A Case Study of Beijing, China

Global warming caused by massive carbon dioxide emissions can lead to a chain of ecological disasters. As one of the main sources of carbon emissions, transportation is of great significance, and the evaluation of its connections with carbon emissions is necessary to achieve “carbon neutrality”. Taking Beijing as an example, this study evaluated traffic efficiency (TE) by utilizing principal component analysis and fuzzy comprehensive evaluation. Using the Tapio decoupling model and coupling coordination degree model, the corresponding relationship between urban low carbon level (LCL) and TE was explored. The results showed the following: (1) The total carbon emission (CE) level exhibited fluctuating variation from increasing to decreasing. The carbon emission intensity (CEI) continued to slow down, and the rapid growth of population density played a key role in low-carbon development. (2) The traffic operations continually showed a positive trend in development. TE increased from a step-like to a slow shape, until it declined in 2020 due to the pandemic. (3) TE and LCL both developed from low coordination to an extreme level of coordination. Per capita carbon emission (CEP) and TE presented an inverted U-shaped curve; meanwhile, with increases in TE, the decline in CEI slowed. In addition, the weak decoupling of TE changed to become strong, due to CE and CEP, and maintained a strong decoupling state from CEI. (4) There is a necessity for the rational planning of land use for transportation infrastructure, the encouragement of a combination of public and private transportation, and the strengthening of the maintenance of the relative infrastructure and the management of traffic behaviors to attain a win–win situation. The results provide a reference for optimizing the traffic structure to achieve “carbon neutrality”.

How does urbanization impact China's carbon emissions: A regional heterogeneity perspective.

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.

Carbon Emission Heterogeneity in Developed Countries: Insights for China’s Neutrality Strategy

This study investigates the heterogeneity and driving factors of carbon emission reduction in developed countries, providing insights for formulating carbon neutrality strategies and plans in China. Firstly, typical developed countries worldwide are categorized into four modes: high energy consumption–high emissions, high energy consumption–low emissions, low energy consumption–low emissions and low energy consumption–high emissions, based on the “per capita energy consumption–emission intensity” classification method. Secondly, the LMDI decomposition model is employed to calculate the carbon emission-reduction driving effects of each mode, thereby analyzing the evolution path of carbon emissions. Finally, carbon emission-reduction policy recommendations are proposed based on the current situation of provincial carbon emissions in China. The research findings are as follows: (1) Carbon emissions in developed countries follow the Environmental Kuznets Curve (EKC) law, exhibiting an inverted U-shaped trajectory. In the per capita GDP range of USD 12,000–20,000, carbon emissions in most countries have reached or are close to their peak, with per capita carbon emissions in different modes decreasing and tending towards a level of 4000 kg/person. (2) There is heterogeneity in the EKC of carbon emissions in developed countries, which can be divided into technological, structural, comprehensive and clean types based on differences in emission-reduction factors. The trajectories of carbon emissions in most developed countries follow a clean path, while those in newly developed countries are constrained by environmental pressures and are expected to be between mixed and structural types before the emission peak, and tend towards a clean type after the peak. (3) Energy clean-up is the most direct and efficient method for carbon emission reduction, which can significantly and efficiently achieve carbon emission reduction. Industrial restructuring and technological progress play a significant role in suppressing carbon emissions. Policy recommendations are proposed as follows: first, reduce the proportion of fossil energy and increase energy cleanliness; second, promote industrial restructuring to reduce energy dependence; third, enhance production technology and improve energy-utilization efficiency; fourth, increase technological breakthroughs to support low-carbon transformation; fifth, correctly recognize the differences among provinces in China and formulate differentiated carbon emission-reduction policies.

The Synergistic Effect of Urban Economic, Social and Space Factors on Residential Carbon Emissions: A Case Study on Provincial Capitals in China

Within the context of China’s commitment to carbon reduction goals, particularly in urban areas, addressing carbon emissions stemming from residents’ travel activities assumes paramount significance. Drawing upon established theoretical frameworks, this study advances several hypotheses delineating the determinants of low-carbon behaviors among urban residents. It analyzes panel data from 30 provincial capitals in China using a time–individual dual fixed effects model. This study empirically scrutinizes the posited theoretical model, aiming to elucidate the factors shaping urban residents’ low-carbon behavioral patterns and provide a decision-making basis for low-carbon construction and management of urban space. The findings underscore several notable associations. The disposable income, population density, and urban built-up areas exhibit significant positive correlations with carbon emissions among residents. Conversely, the urban gross domestic product (GDP) displays a significant negative correlation with carbon emissions. Furthermore, a positive correlation is discerned between the expanse of green spaces and the per capita carbon emissions intensity, while the availability of subway systems exhibits a negative correlation with both the per capita public green space area and the carbon emissions intensity. Notably, the configuration intensity of urban bus systems manifests an inverted U-shaped relationship with residents’ carbon emissions intensity. Specifically, within a certain threshold, an escalation in the bus equipment intensity coincides with heightened carbon emission intensity; however, beyond this threshold, a notable reduction in the per capita carbon emissions intensity ensues. Additionally, a U-shaped relationship is observed between the number of urban parks and residents’ carbon emissions intensity, indicating that an increase in parks may not necessarily contribute to carbon reduction efforts. Moreover, a discernible synergy is observed among various factors influencing carbon reduction efforts. These factors encompass residents’ education levels and disposable incomes, the presence of subway and regular public transportation systems, urban land utilization scales, economic development levels, green space provisions, public transportation infrastructure, population densities, and land equilibrium. This interplay underscores the interconnectedness and interdependence of diverse variables in shaping strategies for mitigating carbon emissions within urban contexts.

Can China's carbon generalized system of preferences reduce urban residents' carbon emissions? Evidence from a quasi-natural experiment

The carbon generalized system of preferences (CGSP) is an innovative incentive mechanism implemented by the Chinese government, which has also become an important part of carbon emission reduction at the living end, and it is of great significance to study whether the Pilot Policy can reduce the carbon emissions of residents. This study firstly accounts for the total carbon emissions and per capita carbon emissions of the residents of 284 cities in China, and on this basis, adopts the SCM method to quantitatively study and analyze the overall and local implementation effects of CGSP in China by taking the first batch of CGSP pilots in China as an example, and further applies the mediation effect model to test the pathways of the role of CGSP. The main findings of the study are as follows: (1) During the period of 2010–2020, the total carbon emissions from urban residents' living in China showed a yearly growth trend, from 36,623.98 ×10−2Mt in 2010–85,241.20 ×10−2Mt in 2020, an increase of 8.83%. Total carbon emissions present a structural difference of "electricity consumption > central heating > private transport > gas (oil, natural gas) consumption". (2) Overall, the implementation of the CGSP had a robust positive impact on the overall carbon emission reduction in the pilot cities, with an average annual emission reduction effect value of 36.53 ×10−2Mt. Locally, the annual net policy effect values of Dongguan, Zhongshan, Heyuan, and Guangzhou are 6169.79 ×10−2, 26,600.17 ×10−2, 17,081.34 ×10−2 and 9393.36 ×10−2Mt respectively. (3) CGSP has a good carbon emission reduction effect by suppressing the impact on residents' carbon emissions through enhancing the city's innovation capacity and promoting electricity saving and consumption reduction, while the mediating effect played by the promotion of green and low-carbon travel in the pilot policy is not significant. Finally, based on the research findings, relevant suggestions are targeted.

Analysis of the Coupling Coordinated Development of the Water-Soil-Energy-Carbon System in Northwest China

The intricate interplay between water resources, land resources, energy systems, and carbon emissions has emerged as a prominent discourse in academic research. Nonetheless, there remains a dearth of comprehensive research dedicated to fostering environmentally friendly and low-carbon development through systematic integration and coordination efforts. This study aimed to fill this gap by integrating the coupling mechanism of the Water-Land-Energy-Carbon (WLEC) system, thereby constructing an evaluation index framework that assesses coordinated developments in Northwest China’s WLEC system. It scrutinizes the security levels within the Water-Land-Energy (WLE) subsystem while conducting quantitative analyses on the degrees of coupling coordination within the WLEC systems, alongside their associated hindering factors. The preliminary findings were as follows: Firstly, the safety development within the WLE system demonstrates a positive trend across Northwest China, with notable advancements primarily observed in the Qinghai and Gansu provinces. In 2021, all five Northwestern provinces exhibited moderate levels of safe development, except for the Qinghai province, which transitioned from moderate to robust. Secondly, the coupling coordination degree of the WLEC system across the five provinces has consistently illustrated an upward trajectory over the years. However, as of 2021, the system remains in a state characterized by marginal disorder. Notably, the Qinghai and Gansu provinces have shown particularly encouraging upward trends, with the Shaanxi province also making notable progress. Conversely, Xinjiang has declined since 2017 due to negative interactions between the subsystems of water, soil, energy, and carbon emissions. Thirdly, spatial variations exist in the distribution patterns between the provinces regarding the influence of obstructing factors on indicators related to coupled coordinated development, such as per capita arable land area, per capita energy consumption, per capita carbon emissions, per capita construction land area, and the proportion of groundwater water in the total amount of water supply, which demonstrated an increasing trend. These findings carry significant practical implications for actively and steadily promoting carbon peaking and carbon neutrality in Northwest China.

Publicly expressed climate scepticism is greatest in regions with high CO2 emissions

We analysed a recently released corpus of climate-related tweets to examine the macro-level factors associated with public declarations of climate change scepticism. Analyses of over 2 million geo-located tweets in the U.S. showed that climate scepticism – and the aggressiveness of climate-related tweets – was greater in states with higher per capita carbon emissions. This pattern remained significant after controlling for political conservatism, GDP per capita, education, and gender, and was replicated across 126 nations from around the world. The findings are consistent with a vested interest hypothesis—misinformation around climate change is most likely to be distributed in regions where there is high fossil fuel reliance, and where the economic stakes of acknowledging climate change are high. Understanding the macro-level patterns that are implicated in climate scepticism can help inform structural interventions for those seeking a low-carbon future.

China's provincial carbon emission driving factors analysis and scenario forecasting

Studying the drivers of China's carbon emissions at the provincial level can clarify differences in carbon emissions due to initial resource endowments and explore pathways to achieve China's 2030 carbon peak and 2060 carbon-neutral commitments (China's 30.60 decarbonization target). In this paper, the carbon emissions of 30 provinces in China during 2000–2019 were calculated using the emission coefficient method. The LMDI model was used to investigate each province's carbon emission drivers. On this basis, the STIRPAT model is used to predict the carbon emissions of each province under three scenarios: low carbon, baseline, and high carbon. The results show that: (1) China's carbon emissions have significant regional differences, and the trend of total carbon emissions is consistent with that of per capita carbon emissions; (2) Economic development contributes the most to regional carbon emission; (3) China's carbon emission trend can be divided into four patterns: gathering type, discrete type, overlapping type, and idiotype. The results enrich the research on carbon emission drivers and forecasts, provide targeted policy recommendations for China to coordinate regional economic development, energy conservation, and carbon emission reduction, and explore a path for China to achieve the 30.60 decarbonization goal.

Quantifying the trends and affecting factors of CO2 emissions under different urban development patterns: An econometric study on the Yangtze river economic belt in China

Environmental issues are rarely investigated within the framework of different urban development patterns, especially concerning CO2 emissions. This research works to reveal the trends and determining factors of urban CO2 emissions under different urban development patterns. Taking the cities in the Yangtze River Economic Belt (YREB) as an example, this study develops an identification system in light of the dynamics of urban population and population density to categorize urban development patterns as growth, potential shrinkage, smart shrinkage, and continuous shrinkage. Then, the Theil-sen medium analysis and spatial econometric modeling are applied to shed light on the trend and influencing factors of CO2 emissions, respectively. The findings indicate that urban growth and potential shrinkage are the main development patterns in the YREB, and the percentage of growing cities and potentially shrinking cities is more than 77 % across the different geographic areas of the YREB. CO2 emissions are remarkably heterogeneous under different urban development patterns. Over 90 % of cities show a growth trend or fluctuation in per capita CO2 emissions under the four development patterns during 2010∼2019. Only a few growing and potentially shrinking cities show a decreasing trend in per capita carbon emissions, suggesting a severe peak carbon challenge in the YREB. CO2 emissions in growing and shrinking cities are significantly influenced by their neighboring cities. Factors affecting CO2 emissions are different in shrinking and growing cities, and spatial spillover effects enhance the role of these factors. Compared with growing cities, improving population concentration, developing appropriate tertiary industries, strictly controlling land expansion, and developing green areas can pave the way for carbon emission reduction in shrinking cities. This study can support designing efficient carbon emission reduction measures for various cities.

Forecasting carbon emissions using asymmetric grouping

AbstractThis paper proposes an asymmetric grouping estimator for forecasting per capita carbon emissions for a country panel. The estimator relies on the observation that a bias‐variance pooling trade‐off in potentially heterogeneous panel data may be different across countries. For a specific country, cross validation is used to determine the optimal country‐specific grouping. A simulated annealing algorithm deals with the combinatorial problem of group selection in large cross sections. A Monte Carlo study shows that in case of heterogenous parameters, the asymmetric grouping estimators outperforms symmetric grouping approaches and forecasting based on individual estimates. Only in the case where the signal is very weak, pooling all countries leads to better forecasting performance. Similar results are found when forecasting carbon emission. The asymmetric grouping estimator leads to more pooling than a symmetric approach. Being on the same continent increases the probability of pooling, and African countries seem to benefit most from using asymmetric grouping and European countries least.

Assessment of carbon emissions from TOD subway first/last mile trips based on level classification

Many cities around the world have developed Transit-oriented development (TOD) based on subway systems, expecting to alleviate problems such as carbon emissions and pollution in the transportation sector. Although previous studies have proved the contributions to mitigate congestion and carbon emission pressure, there is a lack of evaluation on whether TOD can promote green mode choice and effectively reduce carbon emissions of first/last-mile trips in existing studies. This study aims to verify that subway stations with high TOD levels have a positive effect on reducing the carbon emissions of first/last-mile trips. We evaluate subway stations in Hangzhou and classify them into two categories based on their TOD levels. Then the carbon emission of ten typical subway stations is calculated based on survey data of first/last-mile trips and empirical formula. The nested logit model (NL) is used to analyze the correlation between the choice of first/last-mile mode and personal and environmental factors. The case results show that subway stations with a higher TOD level usually exhibit higher daily passenger flows (increased by approximately two-fold) and lower per capita carbon emissions (reduced by approximately 70 %). These findings proved that the high TOD level has a positive impact on carbon emissions of subway first/last mile trips, which could provide data support and insights for TOD development, especially in developing countries.

Impact assessment of poverty alleviation on carbon inequality: evidence from households in China

Inequality and climate change are major challenges for sustainable development, but a dilemma between the objectives has been widely observed. Based on the panel data of China Household Finance Survey (CHFS), inequality in carbon emissions across income groups is witnessed, which provides basics for the study. From a quasi-natural experiment, an increase of 9.9% in household per capita carbon emissions (HCEs) of the impoverished group is found after the Targeted Poverty Alleviation policies (TPA) in China, providing evidence for the enhancement of poverty alleviation measures on equal right for household carbon emissions. Specifically, dual mechanism of transfer payments and employment assistance acting as direct and indirect strategies to enhance family wealth are examined as the pathways to carbon equality. Further, a decline in household carbon emission intensity in the impoverished group is also observed after the treatment, which implies the accelerating role of TPA in low-carbon lifestyle formulation for the impoverished families. The findings of this paper confirm the positive effect of TPA policies on carbon equality for households, and also refute the "equality-pollution dilemma", which provide insights for policy makers in formulating equitable decarbonization policies and allocating the carbon budget.