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To achieve the goal of carbon reduction, China has been piloting the Carbon Emission Trading System (CETS) since 2013. As the economy faces a downward trend, it is significant to explore the impact of CETS on business development. There is still debate in academia about whether this policy can boost the level of business development. This paper, based on all A-share data of listed companies in China from 2009 to 2018, uses the Difference in Differences (DID) model to verify the impact of CETS on the input of capital and labor factors and the level of technology in enterprises and discusses the industry heterogeneity of this impact in detail. Placebo tests, propensity score matching, and triple differences ensure the robustness of the conclusions. In further research, this paper decomposes the policy effect of CETS. It regresses the impact of carbon quota prices and carbon market trading scale on business development. The final conclusion is that CETS has a positive impact on the input of capital and labor in enterprises and a negative impact on the level of technology. After distinguishing industries, this conclusion shows differences according to different characteristics of high emissions and low emissions. In addition, the increase in carbon quota prices hinders business development, while the scale of carbon market trading shows an inverted "U" relationship with business development. The article provides meaningful policy references for China and countries in the early stages of CETS construction. [ABSTRACT FROM AUTHOR]

Carbon flow in forest products trade will affect the report of a country's carbon stock. This will have an impact on a country's contribution of carbon flows. Based on the analysis of China's forest product trade, this paper uses the stock-change approach, the production approach and the atmospheric-flow approach to calculate the changes of China's forest products trade carbon flows. Different carbon flow measurement approaches have very different results. From the perspective of the contribution of forest products to carbon flow, the production approach of calculation for forest products trade carbon flow has the largest contribution to atmospheric carbon flows. However, the calculation results of the stock-change approach and the atmospheric-flow approach are just the opposite, that is, the calculation for forest products trade has a negative contribution to China's carbon sinks, and forest products trade has become a source of carbon emissions. [ABSTRACT FROM AUTHOR]

There have been assessments of the carbon (C) offsetting potential of China's forest sector using historical inventories and future projections. Here, we critically reflect on the assessments we have done and carefully synthesize our findings of China's C sequestration by forest ecosystems and storage by harvested wood products (HWPs). We show that China raised its forest stock volume by 5.069 × 109 m3 during 2006–2020, giving a biomass C uptake of 2.592 petagrams (Pg). With a cumulative C emission of 37.031 Pg during the same period of time, the offsetting ratio of forest biomass is 6.99%. If C stored in HWPs during the period (0.491 Pg) is included, that ratio rises to 8.33%; further counting C sequestered by forest soil (1.277 Pg) boosts the ratio to 11.76%. With a stock volume increase of 14.813 × 109 m3 from 2006 to 2060, projected under a conventional scenario, the forest sector C removal could reach 9.286 Pg without including soil C or 13.017 Pg otherwise, offsetting 10.50% or 14.72% of the cumulative emission of 88.425 Pg. These results indicate that the forest sector has played and will continue to play a significant a role in its decarbonizing drive, but the government has been conservative in setting its targets, particularly for the increase of forest stock volume. Future policy and research efforts must effectively link the country's forest structure, quality, and growth, and thus C sequestration and storage, with improved forest management and wood products manufacturing. [ABSTRACT FROM AUTHOR]

In the present study, it was explored how the volatility of the carbon neutrality concept index (CNCI) was affected by China economic policy uncertainty (CEPU) index, climate policy uncertainty (CPU) index, and geopolitical risk (GPR) index. According to the research of Amendola et al. the GARCH-MIDAS model was improved by introducing the realized kernel volatility of China stock market into the short-term volatility component. On this basis, the GARCH-RKV-MIDAS model was constructed. Meanwhile, both GARCH-MIDAS and GARCH-RKV-MIDAS models were applied to identify the influencing factors for CNCI volatility during the period between January 2018 and June 2022, with CNCI predicted. According to the research results, both the CPU index and the GPR index exert a significant effect on the long-term volatility of CNCI, despite no significant difference made by the CEPU index to the long-term volatility of CNCI. As for the prediction of CNCI volatility, the GARCH-RKV-MIDAS model clearly outperforms the GARCH-MIDAS model. Moreover, the CPU index outperforms the GPR index and the CEPU index in predicting the volatility of the CNCI. [ABSTRACT FROM AUTHOR]

Based on the manually collected carbon information disclosure data for the Chinese listed companies from 2009 to 2015, this paper examines the influencing factors of carbon information disclosure from the stakeholders' perspective. The results show that among external stakeholders, pressures from government and customers can improve the level of firm carbon information disclosure, but the effects of creditor, supplier and competitor on firm carbon information disclosure level are not significant. Among internal stakeholders, the major shareholders, institutional investors, and employees can improve the level of firm carbon information disclosure, but the effects of foreign investors (QFII) on carbon information disclosure are not significant. Among third-party stakeholders, environmental protection organizations and audit institutions can improve the level of firm carbon information disclosure. We hope that findings of this paper will provide guidance to the Chinese firms to develop carbon disclosure policies and encourage researchers to conduct future research on this subject. [ABSTRACT FROM AUTHOR]

Evaluating the operational efficiency of emissions trading pilots has become a crucial task faced by China's emissions trading scheme in its stable transition from pilot programs in limited areas to a nationwide carbon trading market. A number of factors contribute to the difficulty of such evaluations, such as differences in system design, the absence of evaluation systems and the complexity of external factors. A study of the seven major carbon emissions trading pilots in China was conducted. In this paper, the institutional arrangements of carbon emissions trading pilots are differentiated based on six dimensions: distribution systems, flexible mechanisms, emissions control scope, transaction rules, reporting and verification systems, and legal systems. In addition, an evaluation index system is constructed to determine the input and output efficiency of the carbon emissions trading market. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is used to calculate regional difference coefficients, revise the output index data, and effectively control the influence of the non-equilibrium external environment on efficiency. A Super Slack Based Model (Super-SBM) model is used to measure the operational efficiency of the carbon emissions trading market in the pilot area, and the results indicate that (1) for countries and regions with external environments facing uneven development, a differentiated system design should be developed; (2) the lack of changes in the overall ordering of the pilot efficiency demonstrates that institutional arrangements play a decisive role in the operational efficiency of China's carbon emissions trading market; and (3) none of the seven carbon emissions trading pilots has been operating effectively in China. The results indicate that a series of measures should be formulated, including the introduction of mechanisms for increasing quotas; increasing the flexibility of offsetting mechanisms; lowering the market access threshold; expanding the coverage of emissions control; training and restoring the professional monitoring reporting verification (MRV) ability; strengthening efforts associated within incentives and sanctions; and enhancing the legal enforcement of relevant policies. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the trading mechanism of China pilot carbon emission trading market and figure out whether the current market price is rational and valid. By applying directional distance function and stochastic frontier analysis method, we estimated the CO2 marginal abatement cost of 92 coal-fueled generators belonging to 27 power plants located in Shanghai which is one of the pilot carbon emission trading market cities in China. The result indicates that the market price will greatly affect the trading activities within carbon emission trading market. We conclude that the current market price is not rational for coal-fueled power industry which is high-carbon and high CO2 MAC industry. When the exchange of nationwide carbon trading market formally start, policymakers should impose carbon tax or carbon emission quota to adjust the market price and stimulate the CO2 emission reduction willingness of coal-fueled power plants. [ABSTRACT FROM AUTHOR]

Nowadays, as China has become the factory of the world, China's industrial sectors have become the country's main source of energy consumption and emissions including CO2, SO2, NOx and soot. This article will use the non-radial directional dispersion function model (DDF) to explore ways to improve both the energy efficiency and the emission efficiency of China's industrial sectors. Based on current research, this article proposes a new projected input for the treatment of industrial waste gas, and optimizes the projected input and output settings. The data analysis shows that there is room for improvement in China's energy efficiency, and its regional imbalances have expanded. The Chinese government is urged to pay more attention to the improvement of energy efficiency in the central and western regions, and to provide more policy support. Simultaneously, the paper attemtps to establish measurement indexes to improve the efficiency of emission reduction. As a result, the authors suggest that increasing government expenditure can play a positive role in the overall improvement of emission reduction efficiency. [ABSTRACT FROM AUTHOR]

A reduction in the CO2 emission intensity (ton per gross domestic product) from a base year to a target year with different rates of reduction in each individual year can yield different cumulative CO2 emissions over time, which could have different effects on the climate. In this paper, an indicator is proposed to measure the extent to which the cumulative CO2 emissions are generated by a specific reduction trajectory of CO2 emission intensity, especially for emerging economies with a relatively higher economic growth rate. The proposed indicator is important for less-developed countries and regions in which economic growth is heavily reliant on energy and in which significant potential exists for improvement of energy efficiency. The indicator is applied to evaluate the CO2 emission reduction performance in China's 12th Five-Year Plan period, and the result is not encouraging, especially when the likely gap between the energy conservation goals and temperature goals is further examined. Mitigation goals should be set by considering not only the performance in the final year, but also the reduction in cumulative emissions over the evaluation period. [ABSTRACT FROM AUTHOR]

In the Paris Agreement, there are targets set up for 2100 to be well below 2 °C. A more ambitious target of 1.5 °C also appears in the agreement. In order to address whether this target is achievable or not, studies about the 1.5 °C target's emission pathway are essential. Recently a few studies have presented modelling results of the global emission pathway for the 1.5 °C target. This paper presents an analysis for China under the global 1.5 °C pathway and budget, by looking at key options to go beyond the 2 °C target pathway. Similar to the global emission pathway, China's CO2 emissions have to be reduced quickly and reach zero emission between 2050 and 2060. China's energy system needs to make a rapid transition to much greater reduction in fossil fuel use from now on. End-use sectors need to increase electricity use significantly. Power generation will achieve negative emission before 2050. CCS will be widely used, and biomass energy with CCS (BECCS) must be adopted on a large scale by 2040. This is doable in China, but very near-term changes in policy are needed to make such a pathway happen. [ABSTRACT FROM AUTHOR]

Accurate carbon price prediction is a reference that allows market participants to make decisions. This study adopts a total of 1,857 trading days of data from April 2, 2014, to June 15, 2022, in the Hubei carbon market, one of the first and largest pilot carbon markets in China for carbon price prediction. We propose a new framework based on the GA-VMD-CNN-BiLSTM-Attention hybrid model: a genetic algorithm (GA) is adopted to search the optimal parameter combination of variational mode decomposition (VMD); a convolutional neural network (CNN) is established to discover the relationship between influencing factors and carbon prices; a bidirectional long and short-term memory network (BiLSTM) is applied to extract time series information; and an attention mechanism is used to strengthen the influence of important information on carbon prices. Compared to 11 other models, the GA-VMD-CNN-BiLSTM-Attention model has a higher accuracy and stronger model reliability. In addition to deterministic point prediction, this study uses non-parametric kernel density estimation with the Gaussian kernel function (KDE-Gaussian) for interval forecasting. The forecasting can quantify the uncertainty of carbon prices and serve as a more practical reference for decision-makers. By revealing the particularly challenging issue that underlies carbon price forecasting, our analysis also sheds light on current low-carbon policies in China. The GA-VMD method achieves more accurate decomposition results. A hybrid prediction model is established based on CNN and BiLSTM. An attention mechanism is proposed for enhancing the impact of key information. Kernel density estimation with Gaussian function is adopted for interval forecasting. [ABSTRACT FROM AUTHOR]

As China's outwards foreign direct investment (OFDI) has grown dramatically, the environmental impact of the production activities of Chinese multinational enterprises (MNEs) has received substantial attention. Using a multiregional input-output model reflecting firm heterogeneity, this study measures the carbon footprint (CF) and value-added (VA) of Chinese MNEs' foreign affiliates to quantify the trade-off between economic benefits and environmental damage. The results show that Chinese MNEs' affiliates' CF is not large but grows quickly, mainly driven by investment and scale effects. The CF of foreign affiliates is primarily distributed in resource-intensive countries. The economic-environmental effect of Chinese MNEs' foreign affiliates is heterogeneous across countries and sectors. The best performance is found in the manufacturing sectors of most developing countries, with the worst performance in the service sectors of developed countries. Chinese MNEs' affiliates can transfer clean technology from developed host countries to China through reverse technology spillovers. [ABSTRACT FROM AUTHOR]

The developing countries rely heavily on imports of capital goods to spur economic growth. When the economy grows, energy consumption rises, adversely impacting climate change. The low levels of renewable energy share in total energy consumption, developing nations confront a difficult task in achieving the SDGs targets related to an increase in renewable energy share and access to affordable, reliable, and modern energy. Finding solutions to increase renewable energy usage is critical. International trade is an unavoidable part of development, prompting us to consider the impact of imports on renewable energy usage. This study explores the effects of imports of capital goods from China, EU and USA on renewable energy consumption in developing countries by using panel data from 20 countries spanning 2000–2018. It is found that capital goods imported from China in developing countries negatively impact renewable energy consumption while imports from EU have a positive impact on renewable energy consumption. However, in the case of US it is found negative but insignificant. The role of economic, social, and political globalization is explored, and it is found that three types of globalization are positively and significantly linked with renewable energy consumption. Thus, this study recommends that trade policies complement domestic efforts toward increasing renewable energy production and consumption in developing countries. [ABSTRACT FROM AUTHOR]

Due to rapid urbanization and increasing energy consumption, human society poses increasing pressure on the surrounding ecosystems that it depends on. As a result, evaluation of urban development should view cities as complex ecosystems including human and natural elements. This paper establishes a carbon balance index (CBI) for an urban ecosystem based on its socio-economic development and land use and land cover change in Xiamen, a rapidly urbanizing coastal city in southeast China. Dynamic changes in CBI can quantitatively indicate the sustainability of the urbanization process from a carbon cycling perspective. Results show that Xiamen's carbon balance condition experienced three stages: the period 1987–2002 where CBI decreased slightly, 1992–2002 where CBI moderately increased from 1.4 to 4.2, and 2003–2011 where CBI rapidly increased from 5.2 to 17.3. The carbon imbalance increased with the expansion of urban scale and urbanization. By stepwise multiple regression analysis, it was found that the main influencing factors of CBI are urbanization rate, industrial coal consumption, length of roadway network and forest area, and city managers should take these factors into consideration to achieve low-carbon city development. Finally, the CBI for the year 2030 is predicted based on CO2emissions and sequestration scenarios. [ABSTRACT FROM AUTHOR]

With massive urbanization and infrastructure investments occurring in China, understanding GHG emissions from infrastructure use in small and large Chinese cities with different administrative levels is important for building future low-carbon cities. This paper identifies diverse data sources to assess GHG emission from community-wide infrastructure footprints (CIF) in four Chinese cities of varying population (1 to 20 million people) and administrative levels: Yixing, Qinhuangdao, Xiamen and Beijing.CIFaddresses seven infrastructure sectors providing energy (fuels/coal), electricity, water supply and wastewater treatment, transportation, municipal waste management, construction materials, and food to support urban activities. Industrial energy use dominates the infrastructure GHGCIFin all four cities, ranging from 76% of totalCIFin Yixing to 30% in Beijing, followed by residential energy use (6–13%), transportation (4–12%), commercial energy use (2–25%), food (6–11%), cement use (3–8%) and water (about 1%), thereby identifying priorities for low-carbon infrastructure development. Trans-boundary footprint contributions ranged from 31% (Beijing) to 8% (Qinhuangdao), indicating that supply chains to cities are important. GHGs from energy use are dominated by electricity (35–45%) and non-electricity coal use (30–50%). The authors demonstrate that disaggregated infrastructure use-efficiency metrics in each infrastructure sector provide useful baseline performance data for comparing diverse cities. [ABSTRACT FROM PUBLISHER]

This study employs scenario analysis to measure the carbon emissions reduction potential of the final demand structure changes in China from 2013 to 2020. The results indicate that under the business as usual (BAU) scenario (i.e. in which China's final demand structure, energy structure and technical level in 2020 are the same as those in 2012), China's gross domestic product of 2020 calculated by 2005 constant price is 6,427,658,783×10,000 yuan and China's total carbon emissions will reach 1,531,692.29×10,000 tons, with its carbon emission intensity standing at 2.3686 t/10,000 yuan (it is only 25.57% lower than in 2005). Furthermore, under the specific final demand product structure (when "government consumption", "export consumption", "residents' consumption + capital formation" accounts for 12%, 18% and 70%of final demand, respectively), China's total carbon emissions in 2020 will be reduced by between 167,595.33×10,000 and 204,142.45×10,000 tons compared to the BAU scenario, and carbon emission intensity nationwide will be reduced by 33.71-35.49% compared with 2005. Finally, based on the assumptions and conclusions, this study puts forward some suggestions to realize the China's carbon emission reduction commitments. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) Capture, Utilization and Storage (CCUS) is an indispensable part of the carbon removal technologies to achieve carbon neutrality for China. Our study focuses on China's CCUS pathways, and draws out three key conclusions: (1) in terms of the greenhouse gases emission reductions required to achieve carbon neutrality and based on current technology projections, the CO2 emission reductions to be achieved by CCUS are 0.6 ∼ 1.4 billion tonnes and 1 ∼ 1.8 billion tonnes in 2050 and 2060, respectively; (2) from the perspective of source-sink matching in China, the emission reduction potential provided by CCUS can basically meet the demand of carbon neutrality target (0.6 ∼ 2.1 billion tonnes of CO2); (3) with the development of technologies, the cost of CCUS in China has a great potential to be reduced in the future. It is expected that by 2030, the technical cost of the whole CCUS process (according to 250 kilometers transportation) in China will be 310 ∼ 770 Chinese Yuan per tonne of CO2, and by 2060, it will gradually drop to 140 ∼ 410 Chinese Yuan per tonne of CO2. [ABSTRACT FROM AUTHOR]

Based on a sample of Chinese publicly listed firms in eight high energy-consuming industries from 2015 to 2019, this study investigates the relationship between the intensity of product market competition and carbon disclosure and explores the moderating effect of earnings pressure and environmental legitimacy pressure on this relationship. Using content analysis to construct an integrated measure of corporate carbon disclosure, we find that the intensity of product market competition is negatively associated with carbon disclosure. The negative relationship between the intensity of product market competition and carbon disclosure is more pronounced among firms facing fewer earnings pressures and greater environmental legitimacy pressures. The additional tests also show that the effect of product market competition on carbon disclosure is more pronounced among state-owned firms and large firms. Our results are robust to various robustness tests. This study extends the literature on carbon disclosure and has important implications for different stakeholders to promote corporate climate-related disclosure in China. [ABSTRACT FROM AUTHOR]

Despite recent voluntary climate change reporting (VCCR) initiatives, such as the Carbon Disclosure Project (CDP), Chinese companies continue to lag behind their global counterparts. This research contributes to the existing literature examining the effects of CEO background (specifically academic, foreign and political background) on corporate decisions to engage in VCCR. The data sample consists of Chinese listed companies that participated in CDP surveys during 2010–2017 (1041 final firm-year observations). The study's descriptive findings reveal that most Chinese companies are reluctant to report their climate change information to the CDP as only 12% of Chinese companies participated in the CDP annual survey during the study period. However, using a logistic regression model, we find that CEOs with academic experience, foreign exposure and political connections positively affect firms' decisions to engage in VCCR. The results remain robust when we use the propensity score matching technique to account for sample selection problems. Overall, the results are interpreted within the theoretical insights of the upper echelons perspective. [ABSTRACT FROM AUTHOR]

Using sample firms from eight energy-intensive industries included in the State Council of China's Notice on the Pilot Work of Carbon Emission Trading from 2015 to 2019, this study examines the relationship between environmental legitimacy pressure and impression management of carbon information disclosure, and explores the moderating effect of political connection on this relationship. The baseline results show that environmental legitimacy pressure is positively associated with impression management of carbon information disclosure, and political connection moderates this relationship, that is, the positive association between environmental legitimacy pressure and impression management of carbon information disclosure in politically-connected firms is stronger than that in non-politically-connected firms. The results are robust to various sensitivity tests. Further analyses show that (i) the positive association between environmental legitimacy pressure and impression management of carbon information disclosure in non-state owned enterprises (non-SOEs) is stronger than that in SOEs, (ii) firms facing higher negative environmental legitimacy pressure have stronger motivation to conduct impression management of carbon information disclosure, (iii) firms facing environmental legitimacy pressure conduct both selective disclosure and expressive manipulation but they have stronger motivation to conduct expressive manipulation. This study extends the literature on impression management of carbon information disclosure. The findings in this study provide policy implications not only for China but also for countries with large carbon emission. [ABSTRACT FROM AUTHOR]

To mitigate the detrimental effects of climate change exacerbated by excessive carbon emissions, China has introduced a series of environmental regulations designed to promote low-carbon technology diffusion among enterprises. The existing literature ignores the heterogeneity of the scales of enterprises, making it extremely difficult to put forward targeted policies for enterprises of different scales. In this study, we employ a complex network-based evolutionary game approach to analyze the effect of environmental regulations on the diffusion of low-carbon technology, and thereby determine the relationship between the degree of diffusion and the strength of regulations at different scales of enterprise. The results revealed that with respect to the degree of diffusion, there are significant differences in the sensitivity of enterprises in 20- and 100-scale networks to the imposition of three types of environmental regulation (innovation subsidy, carbon tax, and punishment). When the scale of the network is fixed, fluctuation in the change in degree becomes larger, and even irregular, with an increase in enterprise scale. With an increase in network scale for small, medium, and large enterprises, the range of variation in the degree of diffusion becomes smaller. In small, medium, and large enterprises, when the scale of the network increases from 20 to 100, the degree of diffusion will increase in response to the same strength of innovation subsidy, carbon tax, and punishment. With an increase in innovation subsidies, the diffusion degree of large enterprises will improve slightly. Moreover, for large enterprises, increasing the strength of punishment has no obvious effects with regards to improving the degree of diffusion. At all scales of enterprise, in both the 20- and 100-sacle networks, the degree of diffusion will increase with an increase in the strength of the carbon tax. [ABSTRACT FROM AUTHOR]

The measurement indicator system of carbon lock-in was constructed from four dimensions: fixed investment, technique, institution and social behavior; and the projection pursuit evaluation model based on the Real-coded Accelerating Genetic Algorithm, (RAGA-PP model) was established. In this study, China's carbon lock-in level was measured and analyzed based on relevant data from 2003 to 2016. The model application contributes to solving problems such as the subjectivity of weight estimation and premature convergence. The results indicate that, in addition to the increasing degree of social behavior lock-in, for China, the overall carbon lock-in and lock-in levels of fixed input, technique, and institution have decreased dramatically. The overall carbon lock-in level of the eastern provinces is the weakest, followed by the central and northeastern regions, with the western regions being the strongest, and the polarization of provincial carbon lock-in levels being the most significant in the central and western areas. The government should make prudent decisions on the main investment and construction of social infrastructure from a long-term perspective. It is essential to make breakthroughs in the core technologies of complementary and coupled energy utilization to accelerate the formation of new power systems. [ABSTRACT FROM AUTHOR]

China announced the launch of a national emissions trading scheme (ETS) in December 2017 for the regulation of its carbon dioxides emissions. In emissions trading, China is likely to encounter issues different from those encountered by the European Union and other developed countries. This study first identifies the contextual factors that influence the design of China's national ETS and affect its performance. After an overview of the design of China's national ETS, this study focuses on two key features: (1) ex-post adjustment of the initial allowance allocation in proportion to the actual output levels, and (2) treatment of the power sector. Finally, the study analyzes the interaction between China's national ETS and some overlapping policies such as those on air pollution control. The study's key findings are that China's ETS cannot fully achieve cost-effectiveness because of its design, the price behavior of China's ETS will be different from that of a cap-and-trade ETS, and overlapping policies such as the command-and-control regulations for energy conservation and/or air quality improvement will decrease the demand for allowances and limit the opportunity for allowance trading. These findings are based on existing theoretical studies and relevant global ETS experience. [ABSTRACT FROM AUTHOR]

Allocating emission quotas among provinces fairly and efficiently is a critical issue for China. We developed a carbon quota allocation framework at the provincial level considering both the equity and efficiency principles based on a multi-objective non-linear programming model. We established a carbon Gini coefficient and an emission abatement cost function to measure the equity and efficiency of quota allocation, respectively. We then introduced them as objectives into a multi-objective non-linear programming model and obtained the optimal emission quota allocation for 30 provinces in China, by realizing the trade-off between the equity and efficiency principles. Our analysis revealed that Chinese carbon emissions have not yet peaked and that the proportion of carbon emission in each province is similar. Moreover, provinces with higher GDP per capita, carbon intensity, and historical accumulated carbon emissions should shoulder more burden of carbon intensity reduction and higher marginal reduction costs. The rationality analysis indicates that our method outperforms traditional methods according to the principles of both equity and efficiency. We conclude by offering policy recommendations for the establishment of a national unified carbon market. [ABSTRACT FROM AUTHOR]

We use the 2004–2016 China prefecture-level city data to construct a STIRPAT-based spatial econometric model as an empirical test for the driving factors of city carbon emissions. Results show that China's city carbon emissions exert significant spatial spillover effects and high-emission club agglomeration characteristics. The relationship between city carbon emissions and the city's own economic development has an "inverted U" shape, whereas city carbon emissions and the economic development of neighboring cities exhibit a "J"-shaped relationship. The improvement of energy efficiency is not only conducive to controlling the carbon emissions of the city itself but also beneficial to regulating carbon emissions in neighboring cities. The coal-based energy structure will significantly increase the carbon emissions of the city itself and those of the neighboring cities. Population density will increase the city's own carbon emissions but reduce carbon emissions in neighboring cities due to the siphon effect. Traffic facilitation and industry structure in a city will lead to an increase in carbon emissions in neighboring cities. Foreign direct investment will benefit the city's own carbon emission control. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) formed during coal combustion is responsible for over half of the increase in the greenhouse effect, globally. It is crucial to conduct a comprehensive analysis and accurate prediction for coal production and consumption to optimize coal utilization and control emissions. However, several unstable factors inhibit the accurate forecasting of coal. Therefore, taking into account both effect and efficiency of prediction, the Hubbert model is applied in this study to forecast China's peak coal production, peak year, and future production. Additionally, a combined model, with weights determined by a flower pollination algorithm and no-negative-constraint theory, is proposed to forecast coal consumption, which is superior to that of all individual models. Subsequently, accounting for the vital effect of CO2 from coal combustion on economic issues, correlation analysis and an environmental Kuznets curve (EKC) are applied to quantitatively examine the relationship among coal consumption, economic development, and carbon emissions. The results of this estimation suggest that, compared to the US, China's economic development relies more on coal consumption; however, the EKC indicates that China has still not reached the 'inflection point'. Accordingly, it is essential for policymakers to balance the relationship among coal utilization, economic development, and the environment. [ABSTRACT FROM AUTHOR]

Glass is one of the materials most extensively used for packaging. Its manufacturing process is typically energy intensive, with a large quantity of CO2 emissions. In this study, the material and energy consumptioninventory of the manufacturing stage were quantified and CO2 emissions from the glass manufacturing process were calculated. When fuel oil, coal or natural gas is used as the major energy source in the production system, it produces CO2 emissions of 1.2798, 0.6250 and 0.4498 t/t, respectively. CO2 emissions from fossil fuel combustion account for 67.79% of the total emissions and a huge potential exists for emission reduction. Due to differences in energy structure, current research results cannot represent the emissions in China; therefore, six scenarios were created to explore the CO2 emission reduction potential of the glass industry with different energy structures, and six scenarios were created to explore the cost from the adjustment of the energy structure. According to the analysis of the glass furnace, the effective heat accounts for 35.31%, so the basic approach for energy saving and emission reduction is to enhance the effective heat quantity of the glass furnace. Also, energy and virgin feedstock savings were calculated for 1 t of glass container production with different recycling levels. [ABSTRACT FROM AUTHOR]

The electric power industry has often been considered one of the key sectors for energy-saving and emission reduction. It is important to explore the main factors driving the greenhouse gas (GHG) emission changes of this industry. This study applies the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze disparities in the driving forces from national and regional perspectives. In addition to the factors related to the generation sector, this study puts forward the concepts of power self-sufficiency ratio and available-to-consumed ratio to indicate the influence of factors related to the transmission and distribution sector. The results show that economic activity was the main factor promoting the growth of GHG emissions; the power intensity of gross domestic product (GDP) and population change had smaller promotional effects at the national level; the generation structure and the energy intensity of thermal power were two main contributors to inhibiting the growth of GHG emissions of the national power industry, but they had promotional effects in some provinces; and the energy mix of thermal power and the power self-sufficiency ratio contributed slightly to decreases in the GHG emissions of the power industry, despite their promotional effects in some provinces. [ABSTRACT FROM AUTHOR]

The Chinese government launched its implementation plan for a carbon emissions trading mechanism in seven pilot regions in 2011. In this study, we simulate and compare the potential cost savings of emissions trading in the Chinese manufacturing industries of three selected pilot regions, namely Beijing, Chongqing and Guangdong, based on the estimates of industry-specific CO2shadow prices. The characteristics of production technology are also examined. The empirical results indicate that the cost-effectiveness of a 1-ton trade in Beijing, on average, is the largest, followed by Chongqing and Guangdong, but Guangdong would be the largest beneficiary in terms of total trade. [ABSTRACT FROM PUBLISHER]