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The intensification of global climate change has led to a widespread consensus on carbon reduction, with the power industry being the principal contributor to carbon emissions necessitating an inevitable transition of its energy structure. Concomitantly, the escalating frequency of natural disasters caused by extreme weather presents formidable challenges to both secure power supply and the low-carbon transition of the power system. To fulfill the developmental requirements of a low-carbon power system and address the carbon emission risks imposed by natural disasters, this paper proposes a bi-level model for generation expansion planning (GEP) that incorporates constraints on the carbon emission trajectory and the influence of natural disasters. The planning-level model optimizes investment costs of various generation technologies and energy storage (ES), as well as the overall operational expenses over the planning period, with an objective to minimize them. It incorporates carbon emission trajectory constraints and policy constraints, such as carbon peaking, carbon neutrality and renewable energy (RE) penetration rates, in order to optimize the planning installed capacity of power sources. The operational-level model aims to minimize typical daily operating costs while also simulating power unit outputs in routine and disaster scenarios. A case study is conducted in a disaster-prone province in southern China to analyze the power generation expansion planning and the trajectory of carbon emissions from 2020 to 2060 under different scenarios. The simulation results show that compared to thermal power, the planning scheme mainly focused on RE with ES is better suited to achieve the goal of a low-carbon transition of the power grid. Moreover, after considering natural disasters, the cost and carbon emissions of power system planning are higher, and the risk of carbon emissions increases with the severity of disasters. • A multi-time scale bi-level low-carbon GEP model is proposed. • Natural disaster scenarios and low-carbon policy constraints are considered in the GEP model. • Carbon emission trajectory of the planned power system is estimated and introduced as a constraint into the GEP model. • Evaluation indicators that comprehensively consider low-carbon performance and economic feasibility are proposed. [ABSTRACT FROM AUTHOR]

The allowance mechanism is one of the core and sensitive aspects in the design of a carbon emissions trading scheme and affects the compliance cost for each entity covered under the scheme. By examining China’s allowance mechanism from two aspects-allowance allocation and allowance distribution, this paper compares China’s carbon trading pilots with the EU Emissions Trading Scheme and California Cap-and-Trade Program. The comparison identifies the unique features in allowance mechanism and particular issues that affect the efficiency of the pilots. The paper also recommends courses of action to strengthen China’s existing pilots and to build valuable experiences for the establishment of the national cap-and-trade system in China. [ABSTRACT FROM AUTHOR]

With important implications for global climate change mitigation, the development of a cap-and-trade emission trading scheme (ETS) in China is embedded in distinctive political, economic, and institutional contexts and has been attracting increasing attention in recent years. To offer a clear perspective on current research progress, this paper systematically reviews the recent literature on China’s ETS from three main aspects: mechanism design, policy and regional linkages, as well as impact assessment. The current research consensuses are summarized based upon detailed literature analyses and commentaries, and together with observations of international experience and China’s actual condition, the paper provides policy proposals for the development of China’s national ETS during 2016–2020. Finally, several important controversies in the current literature are discussed with regard to the practical demands of China’s ETS development. Five pressing topics that need to be further studied in the coming years are highlighted, including cap setting, allowance allocation among sectors and enterprises, carbon pricing, policy package and unified carbon market construction, as well as ex-ante and ex-post impact assessments. [ABSTRACT FROM AUTHOR]

Household fuel-use (HFU) patterns are strongly associated with multiple effects, including air quality, human health, and regional climate change. This paper presents the results from the first Chinese Environmental Exposure-Related Human Activity Patterns Survey (CEERHAPS), carried out among 91,121 households located in 9108 villages, 636 towns, and 159 counties in 31 provinces. Face-to-face interviews were conducted with each participant to obtain information about the type of fuels used for cooking and heating. The main objectives of this paper were to investigate HFU for cooking and heating in China, to validate the World Health Organization (WHO) model, and to help inform local evaluation models. In China, gas and biomass fuels, the dominant energy fuels for cooking, are used by 44.8% and 32.1% of households, respectively. Approximately 34.1% of families have no household heating during the cold season, and 16.7%, 15.6% and 12.8% of households rely on coal, electricity, and biomass, respectively, for household heating when no central heating was available. The proportion of households using solid fuels has generally decreased during the last two decades. Considerable spatial variation was evident in the HFU patterns for both cooking and heating. The data revealed that the predominant fuel for cooking was biomass (47.6%) in rural populations, whereas urban households were more likely to cook with gas (65.8%). In terms of heating, coal (21.4%) and biomass (19.0%) were the main fuels used in rural households, while electricity (23.6%) and coal (10.5%) were more commonly used in urban areas. The overall HFU results for cooking from this study were comparable to those predicted using the WHO model. We also developed models at the provincial level to estimate HFU for cooking and heating in China; it is expected that the model, if confirmed by future studies, could be used for future research on household air pollution, domestic human exposure and burden of disease. [ABSTRACT FROM AUTHOR]

Highlights • Yield per hectare of sweet sorghum are influence by temperature and precipitation. • Xinjiang and Inner Mongolia are suitable for planting sweet sorghum. • The 2020 and 2050 biofuel goal can be satisfied by certain scenarios in this paper. • Climate change and land-use change are critical to energy crop development plan. Abstract Biofuel from energy crops will play an important role in supplying carbon-neutral fuel for the transport sector. However, there are large uncertainties regarding energy crop development and the energy crop potential and distribution need to be evaluated more comprehensively and systematically. In this study, we propose an integrated approach using the AquaCrop model, a geographic information system approach, and a scenario analysis and we conduct a land availability assessment with multiple factors including climate change, land use, soil, topography, regional crop management, and technological improvement to shed light on the potential production, distribution, and temporal-spatial variation of land suitable for sweet sorghum production in China. We evaluate a full range of spatial scales from 1-km resolution to a national assessment in four representative concentration pathways from 2010 to 2100. The results show that most provinces can plant sweet sorghum. The most suitable provinces for sweet sorghum production are Xinjiang, Heilongjiang, Jilin, Inner Mongolia, Yunnan, and Sichuan. The biofuel potential of sweet sorghum in high-slope farmland (0.30–0.73 EJ) and marginal land (11.98–15.18 EJ) can satisfy the biofuel goal in 2020 (0.12 EJ) and 2050 (6.70–8.60 EJ). Climate change and land-use change are critical factors influencing for the current implementation and long-term planning of energy crops in China. [ABSTRACT FROM AUTHOR]

In an effort to address climate change, in 2013 China launched the world’s largest government-driven carbon emission reduction programme, the National Low Carbon Industrial Parks Pilot Programme (LCIPPP). This paper analyses this newly developed pilot program. To deepen our understanding of the causes and the impact of industrial park CO 2 emissions, we use the STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model and data from 20 pilot industrial parks involved in the LCIPPP for the period 2012–2016. This study quantitatively evaluates the effect of CO 2 emissions on output, energy structure, energy intensity, industrial structure, R&D intensity, and population change in different regions and nationally through an elasticity coefficient method. The results confirm that an increase in output and energy intensity is a dominant contributor to the growth of CO 2 emissions whereas an increase of the share of tertiary industry and R&D intensity has significant effects on reducing CO 2 emissions. The elasticity of energy intensity and renewable energy consumption on CO 2 emissions in the eastern region of China is the highest, indicating that using renewable energy to reduce CO 2 emissions for the industrial parks is more effective in the eastern region as compared to the central and western regions of the country. The elasticity of population is significantly negative in both the central and western areas while it is positive in eastern part of China, thereby illustrating that promoting labour intensive industries will be an effective way to reduce CO 2 emissions for the industrial parks in China’s central and western regions. Our study reveals that differentiated low carbon development pathways should be adopted. Concrete policy implications for reducing CO 2 emissions are also provided. [ABSTRACT FROM AUTHOR]

Accurately understanding the real situation of China’s carbon emissions at the provincial level is not only crucial for realizing carbon mitigation in China, but also of great importance for tackling climate change on a global scale. This paper uses detailed energy consumption data subdivided within each fuel type and sector-specific emission factors, to calculate both direct and indirect energy-related CO 2 emissions for China’s provinces, and offers provincially-targeted policy proposals on emission reductions. The results uncover large divergences in calculation of provincial CO 2 emissions between production-based and consumption-based accounting approaches. Considering the large amount of interprovincial CO 2 emission transfers embodied in secondary energy deployment, the consumption-based accounting performs better in reflecting the actual features and responsibilities of carbon emissions in the provinces of China. From the consumption perspective, Shandong, Hebei, Jiangsu, Inner Mongolia, Guangdong, Henan, Liaoning and Shanxi contributed to the majority of China’s carbon emission growth during the period of 1996–2013, while Xinjiang, Sichuan, Anhui, Tianjin and Hunan were playing an increasingly important role. These two groups of provinces should be regarded as the key regions for national emissions abatement. Meanwhile, the energy industry was the top sectoral contributor to CO 2 emissions growth in most provinces, while service and household sectors made increasingly significant impacts on driving up provincial emissions. Together with ongoing efforts in the energy sector, the mitigation of CO 2 emissions in service and household sectors deserves much more attention, especially in the transportation and urban household sectors. Furthermore, the temporal trends of carbon emissions in various provinces, as well as their factor and sectoral driving forces, varied greatly with each other. Therefore, each of the provinces should formulate emission reduction policies according to their own situation, and target the key driving factors and sectors of carbon emissions at the current stage or in the near future. [ABSTRACT FROM AUTHOR]

Grassland plays a key role for the food security of China because of the large number of livestock raised in those areas. Thus, grassland degradation due to climate change and overgrazing is considered as one of the most severe environmental and economic threat for the future sustainable development of China. Photovoltaic water pumping systems for irrigation can play a fundamental role for the conservation of grassland areas. This paper investigates the geospatial distribution of the technically suitable grassland locations for the implementation of photovoltaic water pumping systems. The technically suitable grassland areas were taken as starting point to assess the optimal locations. The assessment of the optimal locations was conducted using a spatially explicit optimization model of renewable energy systems based on the cost minimization of the whole forage supply chain. The results indicate that the photovoltaic water pumping systems provide high potential for improving forage productivity, contributing to meet the local demand. The optimal areas are highly sensitive to several environmental and economic parameters such as increased forage potential yield, forage management costs, forage water requirements, ground water depth, forage price and CO 2 price. Most of the optimal areas are selected when the market forage price ranges from 300 to 500 $/tonne DM, indicating that the forage produced using PVWP technology for irrigation is already competitive compared to the imported forage. [ABSTRACT FROM AUTHOR]

[Display omitted] • A method for spatiotemporal coordination of multienergy systems is proposed. • Virtual energy storage gain is calculated based on spectral analysis. • The hydropower regulation ability affects the virtual energy storage gain. • The optimal coordination range between multiple energy sources is obtained. Utilizing the spatial heterogeneity and climate periodicity of various available renewable energy sources can enhance the multienergy complementarity, which will further reduce the energy storage demand and contributes to the "virtual energy storage gain." In this paper, we propose a spatiotemporal coordination method based on spectral analysis for a wind-PV-hydropower system that targets the maximum virtual energy storage gain. The complementary effect of hydropower on wind and PV power can be seen as changes in the regulation ability resulting from the hydropower construction development as well as a decreased variance in the total system production output. This method is used to determine the optimal coordination distance of multiple energy sources that are matched over different periods representing typical climate variation. A case study in the Yalong River basin in China obtains the best range of hydropower bundling surrounding wind power and PV power stations under different hydropower construction planning scenarios, and it reveals that increased regulation capacity of hydropower will reduce the optimal coordination distance but can achieve more energy balance if both actual storage and virtual storage is accounted for. This approach can realize energy delivery with the optimal coordination distance to meet intensive and efficient development needs, which can provide guidance and support for the planning and construction of wind-PV-hydropower storage systems. [ABSTRACT FROM AUTHOR]

China’s commitment to the UNFCCC to peak its emissions by 2030, or sooner, signaled a long anticipated shift in China’s model of development with far reaching consequences. Cities in China, and particularly the residential sector in cities, will be charged with making significant reductions in emissions growth even as rates of urbanization continue to climb. Focusing on Beijing and Shanghai, this paper carries out a measures-based economic analysis of low carbon investment opportunities in the residential sector. Results find significant opportunity: between 2015 and 2030, BAU levels of CO 2 emissions could be reduced by 10.2% in Beijing and 6.8% in Shanghai with the adoption of economically attractive low carbon measures. While these headline results underline the case for low carbon investment in the residential sectors of these megacities in China, a closer analysis provides insights for understanding the economics of decarbonisation in cities more generally. [ABSTRACT FROM AUTHOR]

• Constructing a new welfare function considering economy, equity and ecology. • Optimizing carbon emission pathways from multiple perspectives. • Focusing on the emission pathways in China achieving the ambitious targets. With the increasing climate warming, reducing carbon emissions has gradually become a consensus around the world. However, the process of carbon emission reduction will inevitably lead to the transformation of economic structure, industrial structure and energy structure, which will bring huge changes in economic output, employment and other aspects, thus affecting the overall social welfare and equity. Therefore, it is particularly important for each country to determine a scientific and effective carbon emission pathway which take into account both social development and climate targets. As the largest carbon emitter, China has put forward the targets of carbon peak and carbon neutrality. In this regard, the corresponding carbon emission reduction scenarios are designed according to the new situation of China's emission reduction in this paper, firstly. Furthermore, under the premise of maximizing social welfare, an optimization model which integrating economic growth-equity-environment is developed to determine China's emission pathways under different scenarios from 2020 to 2050. Besides quantitatively showing the future total amount and peak value of carbon emissions under different scenarios, the results can also identify its evolution pathway over time. Therein, the peak under most scenarios will concentrate in 2025–2030, which is about 10.7 to 11.8 billion tons. With the increasingly stringent constraints on carbon emission reduction, the cost of emission reduction will increase correspondingly, but the overall social welfare will increase slightly. Finally, our findings provide practical guidance on achieving carbon emission reduction and enlightenments on policymaking. [ABSTRACT FROM AUTHOR]

To cope with global warming, China has promulgated Enhanced actions on climate change : China ’ s intended nationally determined contributions and will start the national carbon emissions trading market in 2017. Carbon emissions are distributed by the form of free quota and paid quota. However, few literatures have focused on how the economy and the environment would be changed by the change of free quota ratio. This paper establishes the 10 scenarios of different free quota ratio of carbon emissions rights and uses a dynamic, recursive computable general equilibrium (CGE) model to simulate the carbon emissions trading market, to explore the relationship between free quota ratio and carbon trading price, and the impact of carbon trading scheme (ETS) on China’s economy and environment. The results show that free quota ratio will not have a direct impact on gross domestic product (GDP) and other economic and environment indicators but carbon trading prices. The prices and the rate of free payment in the current pilot cities in China are still relatively conservative. It is possible to reach emission peak, 8.21 billion ton, in 2025 and accumulative CO 2 reduction from 2017 to 2030 is 20.02 billion tons, or 59.60% of 2010 world’s total CO 2 emission. Cement, minerals, electricity and nonferrous metals under ETS will suffer great losses, so subsidy should be considered. Finally, we suggested that China should reduce the total carbon rights to increase the carbon price in 2017, and gradually reducing the proportion of free quota, from 90% in 2017 to 50% or less in 2030, by which the peak year of CO 2 emission can meet in 2025. We also suggest that ETS is an effective strategy for CO 2 reduction and the ratio should be gradually reduced in ETS to prevent violent fluctuation of carbon price in China. [ABSTRACT FROM AUTHOR]

• CO 2 emissions and PM 2.5 concentrations are analyzed using 10 km × 10 km grid data. • Spatial features are explored at national, urban agglomeration, and city levels. • Four-quadrant distribution and spatial autocorrelation analysis methods are used. • Only a few areas achieved decreases in both CO 2 emissions and PM 2.5 concentrations. • PM 2.5 concentrations are more strongly spatial correlated than CO 2 emissions. Owing to global climate change and increased environmental pollution, China faces the dual responsibility of reducing CO 2 emissions and controlling PM 2.5 pollution. This study compares the spatial characteristics of PM 2.5 concentrations and CO 2 emissions using 10 km × 10 km grid data. The increase and decrease of CO 2 emissions and PM 2.5 concentrations are divided into four quadrants, which indicates four different conditions. Then, spatial autocorrelation method is conducted to analysis the spatial relationships. The empirical results show that (1) In the four quadrants, the increase of CO 2 emissions and the decrease of PM 2.5 concentrations accounted for the highest proportion (25.9%). (2) The spatial differences in CO 2 emissions are large, but the PM 2.5 concentrations show strong spatial aggregation. (3) China's three major urban agglomerations contain more than half of the areas in which both CO 2 emissions and PM 2.5 concentrations increased, and the Pearl River Delta urban agglomeration exhibits the best synergistic reduction effect. By contrast, the Beijing–Tianjin–Hebei urban agglomeration has the worst synergistic reduction of CO 2 emissions and PM 2.5 concentrations. (4) At the urban level, as a typical city in the Beijing–Tianjin–Hebei urban agglomeration, Tianjin's overreliance on heavy chemical industries has led to co-increases in its CO 2 emissions and PM 2.5 concentrations. Shaoxing and Jiangmen, in the Yangtze River Delta and Pearl River Delta urban agglomeration, are among the few cities where CO 2 emissions and PM 2.5 concentrations have both been reduced. Finally, this paper suggests some policy implications of these findings. [ABSTRACT FROM AUTHOR]