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The industrial sector has significantly contributed to global climate change and urban air pollution while driving economic development. This paper takes the synergistic CO2 and SO2 emission reduction in China's industrial sector as an entry point quantifies the synergistic CO2 and SO2 emission reduction effect using the Kaya extension model and analyzes the synergistic emission reduction expansion mechanism in depth. It is found that the synergistic effect of CO2 and SO2 in the industrial sector is prevalent in most provinces in China. Every 10,000 tons of CO2 emission reduction leads to 1600 tons of SO2 emission reduction. Regional heterogeneity also clarifies that there are differences in synergistic emission reduction among different provinces, with most regions having a significant positive effect of synergistic emission reduction, while a small number of regions have an inverse effect, and direct SO2 reduction measures should be considered separately. The interaction effect analysis found that energy efficiency improvement in the industrial sector is the critical path to synergistic effect enhancement. At the same time, R&D expenditure and fixed asset investment have a weak effect on synergistic emission reduction. The interaction of the three factors can synergize the emission reduction effect by more than twice. The study's findings can be used as a reference for China's industrial sector to achieve the goal of reducing pollution and carbon emissions and for the practice of regional synergistic emission reduction, as well as for the formulation of international synergistic emission reduction policies. [ABSTRACT FROM AUTHOR]

The issue of climate change has become an increasing concern for the international community. At the same time, climate change and, particularly, global warming affects the amount of energy required in China and the structure of this demand, thereby influencing the amount of CO 2 (carbon dioxide) emissions produced. To investigate the possible influence of climate change on China's CO 2 emissions, this paper utilizes the degree-day method to calculate the heating degree days (HDD) and cooling degree days (CDD) in 29 provinces in China from 1995 to 2011. A series of appropriate econometric models are subsequently estimated to measure the impact of climate change on CO 2 emissions per capita. To enhance the explanatory power of the estimation results, several indicators, such as income per capita, industry structure, urbanization rate and population density, are incorporated in the estimation equations as control variables. The estimation results indicate that the feedback effect of climate change on China's carbon emissions is statistically significant but not large in magnitude. During the sample period between 1995 and 2011, approximately 1.687% of China's increased total CO 2 emissions could be attributed to climate change. Moreover, the impacts of climate change on different regions of China are notably different. For example, the influence of HDD on CO 2 emissions per capita are most significant in the middle and southern regions, while the impacts of CDD are most significant in the eastern and northern regions. However, there is no obvious evidence that climate change has affected CO 2 emissions in the western region. [ABSTRACT FROM AUTHOR]

The Belt and Road Initiative was proposed by China in 2013 to promote the interconnected development and prosperity of all participating countries. In the context of global response to climate change, understanding the embodied carbon transfer change along economic trade before and after the Belt and Road Initiative is essential to reduce carbon overall emission. In this paper, we used a multi-regional input-output model to study the embodied carbon transfer in trade between the Belt and Road countries that have signed cooperation documents with China from 2004 to 2017. The results showed that from 2004 to 2017, the growth rate that China's carbon emissions imported from other Belt and Road Initiative countries was about 1.9 times that of its exports. From 2004 to 2017, the proportion of China's carbon imports from other Belt and Road countries in China's total carbon imports increased from 3.41% to 3.73%, and exports decreased from 6.52% to 6.03%. In both 2004 and 2017, Electricity, Gas and Water sector, Heavy Industry sector, Transport sector were the three largest carbon transfer sectors in China's multilateral trade. In 2017, the embodied carbon transfer by multilateral trade among other Belt and Road countries except China increased by 15.53% compared with 2004. By comparing the characteristics of carbon transfer between China and the Belt and Road countries from 2004 to 2017, this study will provide insights for carbon emission reduction policy setting of Belt and Road Initiative countries. • We took countries that have signed documents with China as research objects. • MRIO model was used to analyze the carbon transfer change from 2004 to 2017. • The embodied carbon transfer in 2017 of BRI increased by 15.53% compared with 2004. • The growth rate of China's carbon imports from BRI was 1.9 times that of exports. [ABSTRACT FROM AUTHOR]

As the climate is warming up and climate disasters occur more frequently, China's carbon peak and carbon neutrality targets are gaining attention, with provinces starting to set carbon peak targets and constantly exploring low carbon economic development opportunities. However, the existing evaluation index system mainly focuses on low carbon cities and green cities, and there is no evaluation index system for carbon peak and carbon neutrality. This study adopts a combination of theoretical and empirical research and draws on domestic and international research results to construct the first index system for evaluating China's provincial carbon peak and carbon neutrality. The evaluation index system includes 18 indicators from three dimensions: climate ambition, low carbon status, and emissions trends. Using this index system, we calculated the carbon peak and carbon neutrality indices of 30 Chinese provinces. Further, the 30 provinces were classified into six categories, and corresponding policy recommendations for achieving the "Dual carbon" targets were put forward. The evaluation index system constructed in this paper can provide a reference for future carbon peak and carbon neutrality assessments. By disclosing the integrated index of each province, we hope to effectively curb the increasing local carbon emissions; avert inaction in areas with good natural endowments; and motivate the advancement of the latter, to boost the implementation of the "Dual carbon" targets coherently for the whole country. [Display omitted] • An integrated evaluation system on China's Provincial "Dual carbon" was proposed. • 30 provinces in mainland China were ranked based on the integrated index. • 6 groups were classified to show provincial differences on dual carbon performance. • Policy recommendations to fulfill China's climate commitment were put forward. [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]

Global climate change caused by human activities has posed a huge threat to the environmental governance and sustainability of human-being. Fortunately, the carbon emission trading scheme, a powerful tool for reducing carbon emissions, has been established in many countries to slow catastrophic climate change thus promoting human well-being. The nexuses between energy and European carbon markets have been extensively studied due to the grim reality of global climate change. However, no literature to date has examined the multi-scale associations between China's energy and regional CET markets. This paper aims to fill this gap by utilizing a proposed wavelet-based multi-scale investigation framework to explore the information spillovers and dynamic dependence over different time horizons. The original energy and carbon return series are decomposed by the MODWT method. The TVP-VAR-based connectedness and the DECO-FIGARCH model are then built at each selected wavelet component. The wavelet coherence method is also applied to depict the time-frequency dependence. The empirical results demonstrate that the information spillovers at multivariate time-scales are heterogeneous. Generally, GDEA and the crude oil markets act as the information spillover net-transmitters at the raw data level and all wavelet scales. The dynamic spillovers present significant time-varying features and a similar evaluation trajectory. Besides, we offer strong evidence of a low dynamic equicorrelation between China's energy and regional CET markets, suggesting the portfolio diversification benefits. Furthermore, the information spillover and dependence are proved to be more significant at longer time horizons. Finally, the energy-carbon portfolios offer diversification opportunities, and the risk reduction effectiveness varies with wavelet scales. Moreover, the optimal-weighted portfolio exhibits the best risk management performance. Image 1 • Wavelet-based multi-scale spillover and dependence analysis framework is proposed. • MODWT is applied to decompose the original data into various wavelet components. • TVP-VAR-based connectedness is utilized to quantify the information spillovers. • Wavelet-based-DECO-FIGARCH model is employed to examine the dynamic dependence. • Wavelet coherence method is used to depict the time-frequency dependence structure. [ABSTRACT FROM AUTHOR]