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Carbon dioxide (CO 2) emissions and climate change risk have become constraints on global economic sustainable development. Environmental regulation (ER) is a key method for achieving synergy in CO 2 and pollution reduction in China. This paper is the first study to explore the effects of ER on CO 2 emissions by exploiting the National Environmental Protection 11th Five-Year Plan (NEP11-FYP). The implementation of the NEP11-FYP significantly decreases CO 2 emissions by 19.73% in key environmental cities relative to other cities. Heterogeneity analyses suggest that this negative impact is larger in western cities and cities with more pressure to reduce CO 2 emissions. Scale effects, structural effects, and technical effects might be three potential influencing channels through which ER contributes to the CO 2 reduction effect. In addition, the results of the spatial externality of the NEP11-FYP demonstrate a positive spillover effect in neighboring cities within a distance of 300 km and a negative spillover effect in cities more than 500 km away. Our empirical findings provide policy implications for implementing low-carbon transition strategies and reducing CO 2 emissions. [ABSTRACT FROM AUTHOR]

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Technical change has a pivotal role to play in low-carbon development. Recent research has offered different insights regarding the effect of technical change on CO2 emissions but ignored the bias of technical changes which lead to changes in CO2 emissions. To fill the gap, this paper uses the 2008 to 2015 provincial-level data on China's 22 industrial sub-sectors to investigate both the effect of directed technical change on CO2 emissions and its heterogeneity. We find that the technical change in most industrial sectors in China was capital-biased, although a labor-biased trend was evident. Labor-biased technical change is conducive to CO2 reduction, while capital-biased technical change has the opposite effect. Moreover, this effect is different by developmental periods, industries, and regions. Therefore, we propose that the government promotes labor-biased technical change based on the differentiated characteristics. [ABSTRACT FROM AUTHOR]

In view of the climate emergency, it is necessary to model and forecast road transport emissions for implementing more effective climate policy. This paper fills an important research gap by quantifying the challenges, barriers and remedies of achieving road transport emission reduction and air quality improvement in developing countries. It contributes to the debate on top-down and bottom-up approaches to reconcile carbon and other transport related emissions. China and India are chosen as case studies. Using various official statistics and proxy variables, we estimate and compare the road transport carbon emissions of the two countries systematically from 2009 to 2020. Insights from detailed mode-specific estimations of the bottom-up approach are valuable for targeted policies and measures. An analysis of the major components of road transport carbon emissions points to strategies of promoting electric vehicles, reducing transport demand, accelerating modal shift to low-carbon modes, and cleaner electricity to achieve climate policy. [ABSTRACT FROM AUTHOR]

• The innovative effects of three types of carbon-abatement policies (energy-saving goals (ESGs), new energy subsidies (NESs), and carbon emission trading schemes (ETSs)) were examined. • The contributions of both ESGs and NESs policies were relatively stable in the 11th five-year period (2006–2010), but weakened in the 12th five-year period (2010–2015) that followed. • In terms of synergies between policies, no significant estimates for policy coordination were discovered. • Only administrative regulation had a positive effect on the tendency to pursue technology investment. • With an improvement in enforcement strength, the innovation influences from TIPs were not static, but changed differently, even oppositely, on each side of the threshold. Low-carbon technology innovation is considered to be one of the most effective remedial measures for reducing climate change. Based on panel data from 30 provinces in China which were collected firms in operation between 1999 and 2016, this paper used a difference-in-differences (DID) approach to assess whether China's current carbon-abatement policies (i.e. energy-saving goals (ESGs), new energy subsidies (NESs), and carbon emission trading schemes (ETSs)) can direct innovation activities towards low-carbon technologies; then, this paper analyzed the channels involved from the perspective of investment preferences. In terms of policy-induced effects, the contributions of both ESGs and NESs policies were discovered to be relatively stable in the 11th five-year period (FYP) (2006–2010), but weaker in the following 12th FYP, and the ETSs' effect on innovation was found to be not significant. In particular, the estimates for policy coordination were not significant, and the innovation-inducing effects of NESs were moderated by market performance. For the analysis of channels, only administrative regulation had a reinforcing effect on the propensity to pursue technological investment. As enforcement increases, the innovation influence from technology investment preferences (TIPs) has also changed: it is distinct, and even opposite, on each side of the threshold. [ABSTRACT FROM AUTHOR]

China is currently recognized as the leading global energy consumer and CO2 emitter. A significant amount of carbon emissions can be attributed to urban public buildings. Establishing an equitable and efficient carbon emission allocation mechanism is a crucial step to meeting the ambitious targets in China's 2030 carbon peak plan. In this study, we estimate the total amount of CO2 emissions from urban public buildings by 2030 and propose a preliminary scheme of carbon quota assignment for each province. By means of applying the zero-sum gains data envelopment analysis (ZSG-DEA) model, the carbon emission quotas allocation of urban public buildings in China's 30 provinces is proposed, and the corresponding pressure to reduce provincial carbon emissions is analyzed. The results indicate that Qinghai has the lowest carbon emission rate (0.01%) for urban public buildings, while Guangdong has the highest (9.06%). Among the provinces, Jiangsu, Jiangxi, and Tianjin face the least pressure in reducing carbon emissions from urban public buildings. On the other hand, Hebei, Beijing, and Anhui are under great pressure to decrease carbon emissions. Notably, Hebei is predicted to have the highest emission reduction requirement of 95.66 million tons. In terms of pressures on carbon emissions reduction for urban public buildings, Jiangsu, Jiangxi, and Tianjin exhibit the least pressure. Hebei, Beijing, and Anhui are facing intense pressure to decrease carbon emissions. These findings offer policymakers valuable insights into developing a fair and efficient carbon allowance allocation strategy, while also contributing to China's efforts to mitigate carbon emissions and combat climate change. [ABSTRACT FROM AUTHOR]

Under the threat of global climate change, China has proposed a dual carbon goal of peak carbon and carbon neutrality. As the vital carrier for territorial spatial planning, production–living–ecological (PLE) spaces drive carbon emissions and are important to the dual carbon goals. In this study, carbon emissions and sinks of PLE spaces in cities in Heilongjiang Province from 2005 to 2020 were calculated and spatial–temporal changes were analyzed. The carbon emission structure was analyzed in segmentation sectors. The land use changes and socioeconomic factors on carbon emissions were analyzed, and emission reduction strategies were implemented. The results show the following: (1) Carbon emissions from production and living spaces increased yearly. Carbon sinks were smaller than emissions, but capacity was stable. (2) Higher-emission cities were concentrated in southwest Heilongjiang, and carbon emission differences between regions gradually increased. (3) Among carbon emission sectors, agricultural and household made up smaller proportions, while animal husbandry, industrial, transportation, and traffic travel contributed most. Carbon emission structures were transformed by adjusting urban development and industrial structure. (4) For most cities, industrial space was the main emission space, but agricultural production and urban–rural living spaces dominated in some cities. (5) GDP, urbanization rate, and area of city paved roads suppressed emissions in cities with decreased carbon emission grades. The industrial structure and coal consumption inhibited emissions in cities with maintaining and increasing carbon emissions grades. [ABSTRACT FROM AUTHOR]

Intensifying climate change significantly impacts residential electricity consumption, especially in developing countries, such as China, that are experiencing rapid income growth. By combining meteorological and monthly household consumption survey data, this study explores the response function of residential electricity consumption to temperature in China from a micro perspective. Future residential electricity demands and related CO2 emissions are then forecast under different climate scenarios. Overall, the response function is U-shaped, and one additional day above 34 °C will increase monthly residential electricity consumption by 2.11%. Global warming will more likely increase the electricity burden on low-income groups. There will be notable seasonal changes in electricity demand in the future, and the largest increase will occur in August. The total demand for residential electricity caused by temperature change will show a fluctuating growth trend, from 0.8% and 1% in 2025 to 2% and 2.9% in 2060 under the RCP4.5 scenario and RCP8.5 scenario, respectively; meanwhile, this demand will be accompanied by a cumulative increase in carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

Global climate change has become one of the greatest challenges to human development, the study of the dynamic evolution of carbon emissions at the building operation stage and its driving mechanism is of great significance for the reasonable control of carbon emissions, and helpful to provide more scientific and reasonable decision support for proposing an implementable carbon neutral pathway. According to the geospatial differences of carbon emissions at the building operation stage, this paper constructs a research framework on the influencing factors and driving mechanisms of carbon emissions at the building operation stage based on CV, kernel density, Geodetector and other methods. The study shows that first, that the carbon emissions at the building operation stage are unequally distributed in space in provinces of China, and the building carbon emissions vary greatly in structure among different regions. Second, different factors vary greatly in influence. Third, there are great differences in the driving mechanisms of carbon emissions over time, but factors such as urban population and refrigerator ownership are both key influencing factors that have a comprehensive influence on the spatio-temporal changes of carbon emissions. Fourth, all the factor pairs are bifactor-enhanced or non-linearly enhanced, and they can be classified into three types of high, medium, and low levels. Fifth, a carbon neutral pathway map for the building sector is proposed based on the spatial distribution of carbon emissions and its driving mechanism, to provide reference for foreign regions with similar conditions. • Calculated the carbon emission at the building operation stage in Chinese provinces. • Analyzed trend and composition of carbon emission at the building operation stage. • Analyze spatial-temporal changes of carbon emissions by GIS. • For the first time, analyzed driving factors of carbon emissions by Geodetector. • Proposed a carbon neutral pathway map for the building sector. [ABSTRACT FROM AUTHOR]

Universities, the important locations for scientific research and education, have the responsibility to lead ecological civilization and low carbon transition. Ecological footprint evaluation (EFE) is usually used to measure sustainability of campuses. Although it can provide guidance and reference for overall campus planning, it lacks effective significance for individual behavior, especially when the reduction of carbon emissions is the aim. On the other hand a possible solution can be represented by machine learning. It can identify the key factors that will influence individual's overall carbon emissions caused by students' daily behavior, it can be used to find effective ways to reduce individual carbon emissions. This paper applied EFE and machine learning to comprehensively evaluate campus sustainability and students' carbon emissions. Huazhong University of Science and Technology (HUST), a "University in the Forest", was used as a study case in China. Even if HUST is endowned with a forest coverage of 72%, here we showed that its Ecological Footprint Index was −12.52, indicating strong unsustainability. This is mainly due to the high energy and food consumption, caused by the large population living in the campus and the lacking of energy saving measures. The per capita ecological footprint was relatively high, compared with other universities in the world, which meant more efforts needed to be done on ecological sustainability. Low carbon emission is a key feature for a sustainable campus. Based on the questionnaire survey delivered to 486 students who live in the campus, their daily active data were collected in terms of students' personal clothing, food, housing, consumption and transportation. And their associated carbon emissions were calculated based on emission intensities of Chinese population. Based on 486 detailed datasets, machine learning was then used to identify the key daily behavior to influence students' total carbon emission. Results showed that making behavior changes in air conditioning, food and electric bicycle were the most effective ways to reduce carbon emissions. Finally, while effective suggestions were proposed based on qualitative and quantitative evaluations, it is concluded that it is imperative for universities in China to formulate effective low-carbon policies, to achieve sustainable development and to confront global climate change. • A review of main initiatives of sustainable campus construction worldwide. • An evaluation combining campus ecological footprint and individual carbon footprint. • The ecological footprint of "China Forest University" reflects unsustainable. • Machine learning identifies internal driving force of campus carbon emissions. • Targeted guidelines on campus low-carbon construction are provided. [ABSTRACT FROM AUTHOR]