Cooling Effect of Green Spaces on Urban Heat Island in a Chinese Megacity: Increasing Coverage versus Optimizing Spatial Distribution.

Enhancing the cooling effectiveness of green spaces (GSs) is crucial for improving urban thermal environments in the context of global warming. Increasing GS coverage and optimizing its spatial distribution individually proved to be effective urban cooling measures. However, their comparative cooling effectiveness and potential interaction remain unclear. Here, using the moving window approach and random forest algorithm, we established a robust model (R2 = 0.89 ± 0.01) to explore the relationship between GS and land surface temperature (LST) in the Chinese megacity of Guangzhou. Subsequently, the response of LST to varying GS coverage and its spatial distribution was simulated, both individually and in combination. The results indicate that GS with higher coverage and more equitable spatial distribution is conducive to urban heat mitigation. Increasing GS coverage was found to lower the city's average LST by up to 4.73 °C, while optimizing GS spatial distribution led to a decrease of 1.06 °C. Meanwhile, a synergistic cooling effect was observed when combining both measures, resulting in additional cooling benefits (0.034-0.341 °C). These findings provide valuable insights into the cooling potential of GS and crucial guidance for urban green planning aimed at heat mitigation in cities.

Mitigation Measures Could Aggravate Unbalanced Nitrogen and Phosphorus Emissions from Land-Use Activities.

Socioeconomic factors and mitigation potentials are essential drivers of the dynamics of nutrient emissions, yet these drivers are rarely examined at broad spatiotemporal scales. Here, we combine material flow analysis and geospatial analysis to examine the past and future changes of nitrogen and phosphorus emissions in China. Results show that anthropogenic nitrogen and phosphorus emissions increased by 17% and 32% during 2000-2019, respectively. Meanwhile, many regions witnessed decreasing nitrogen emissions but rising phosphorus discharged to waterbody, leading to a 20% decrease in the nitrogen/phosphorus ratio. In addition to many prominent factors like fertilizer use, the increasing impervious land area around cities is a notable factor driving the emissions, indicating the urgency to limit building expansion, especially in North China Plain and other less-developed regions. Improving land-use efficiency and consuming behaviors could reduce nitrogen and phosphorus emissions by 65-77% in 2030, but the nitrogen/phosphorus ratio will increase unintendedly due to larger reduction potentials for phosphorus, which may deteriorate the aquatic ecosystem. We highlight that nitrogen and phosphorus emissions should be reduced with coordinated but differentiated measures by prioritizing nitrogen reduction through cropland and food-system management.

Hybrid life-cycle and hierarchical archimedean copula analyses for identifying pathways of greenhouse gas mitigation in domestic sewage treatment systems.

Electricity consumption and anaerobic reactions cause direct and indirect greenhouse gas (GHG) emissions within domestic sewage treatment systems (DSTSs). GHG emissions in DSTSs were influenced by the sewage quantity and the efficacy of treatment technologies. To address combined effects of these variables, this study presented an approach for identifying pathways for GHG mitigation within the DSTSs of cities under climate change and socio-economic development, through combining life cycle analysis (LCA) and the Hierarchical Archimedean copula (HAC) methods. The approach was innovative in the following aspects: 1) quantifying the GHG emissions of the DSTSs; 2) identifying the correlations among temperature changes, socioeconomic development, and domestic sewage quantity, and 3) predicting the future fluctuations in GHG emissions from the DSTSs. The effectiveness of the proposed approach was validated through its application to an urban agglomeration in the Pearl River Delta (PRD), China. To identify the potentials of GHG mitigation in the DSTSs, two pathways (i.e., general and optimized) were proposed according to the different technical choices for establishing facilities from 2021 to 2030. The results indicated that GHG emissions from the DSTS in the PRD were [3.01, 4.96] Mt CO2eq in 2021, with substantial contributions from Shenzhen and Guangzhou. Moreover, GHG emissions from the sewage treatment facilities based on Anaerobic-Anoxic-Axic (AAO) technology were higher than those based on other technologies. Under the optimized pathway, GHG emissions, contributed by the technologies of Continuous Cycle Aeration System (CASS) and Oxidation Ditch (OD), were the lowest. Through the results of correlation analysis, the impact of socioeconomic development on domestic sewage quantities was more significant than that of climate change. Domestic sewage quantities in the cities of the PRD would increase by 4.10%-28.38%, 17.14%-26.01%, and 18.15%-26.50% from 2022 to 2030 under three Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5. These findings demonstrated that the capacities of domestic sewage treatment facilities in most cities of the PRD should be substantially improved from 0.12 to 2.99 times between 2022 and 2030. Under the optimized pathway, the future GHG emissions of the CASS method would be the lowest, followed by the OD method.

Performance, comparison and utilization of reduced sulfur (-2) compounds (S2-, FeS and SCN-) in autotrophic denitrification process by thiosulfate-driven autotrophic denitrifier.

The coexistence of reduced sulfur (-2) compounds (S2-, FeS and SCN-) are found in some industrial wastewaters due to pre-treatment of Fe(II) salts. These compounds as electron donors have attracted increasing interest in autotrophic denitrification process. However, the difference of their functions still remain unknown, which limit efficient utilization in autotrophic denitrification process. The study aimed to investigate and compare utilization behavior of these reduced sulfur (-2) compounds in autotrophic denitrification process activated by thiosulfate-driven autotrophic denitrifiers (TAD). Results showed that the best denitrification performance was observed in SCN-; while the reduction of nitrate was significantly inhibited in S2- system and the efficient accumulation of nitrite was observed in FeS system with cycle experiments continuing. Additionally, intermediates containing sulfur were produced rarely in SCN- system. However, the utilization of SCN- was limited obviously in comparison with S2- in coexistence systems. Moreover, the presence of S2- increased the accumulation peak of nitrite in coexistence systems. The biological results indicated that the TAD utilized rapidly these sulfur (-2) compounds, in which genus of Thiobacillus, Magnetospirillum and Azoarcus might play main roles. Moreover, Cupriavidus might also participate in sulfur oxidation in SCN- system. In conclusion, these might be attributed to the characteristics of sulfur (-2) compounds including the toxicity, solubility and reaction process. These findings provide theoretical basis for regulation and utilization of these reduced sulfur (-2) compounds in autotrophic denitrification process.

Uncovering the risk spillover of agricultural water scarcity by simultaneously considering water quality and quantity.

Not only insufficient water quantity but also inadequate water quality can pose constraints on agricultural production and result in potential economic losses. Such economic losses in agriculture may adversely impact downstream producers through reduced input supplies. In this study, we developed an index assessing potential economic losses in agriculture under both quantity- and quality-induced water scarcity, termed integrated Agricultural Water Scarcity Risk (AWSR). Combining integrated AWSR with a multi-regional input-output model, we estimated the spillovers of integrated AWSR along supply chains. Our results showed that the intersectoral transmission of virtual integrated AWSR (sectoral spillovers in terms of integrated AWSR) were 5 times the virtual quantity-based AWSR. Pollution significantly intensifies the indirect supply-chain repercussions of agricultural water scarcity. Moreover, we identified some primary virtual integrated AWSR exporters (e.g., Jiangsu-vegetables and Shandong-swine, of which the integrated AWSR had considerable spillover effects on downstream sectors) and importers (e.g., Henan-chemical industry and Henan-textiles, which were vulnerable to upstream integrated AWSR), that could not be detected in quantity-based AWSR results. This study underscores the importance of water quality in the assessments of AWSR. Strategies to mitigate the spillovers of AWSR might be inefficient without the consideration of water quality.

How to obtain industrial waste data at the county scale: Two downscaling models and their application in Dongguan, China.

The availability of environmental emission data is critical in evaluation of countries' ecological security and the implementation of environmental management. However, access to environmental emission data at the county level is not provided by statistical publications and bulletins. Therefore, in this paper, we develop two novel data downscaling models, an environmental Kuznets curve downscaling model (EKCDM) and a scale model (SM), to obtain county-level environmental emission data. The EKCDM relies on the EKC hypothesis and the assumption that the same model applies across scales, whereas the SM depends on the assumption that the share of a region's environmental pollution is equivalent to its share of economic output. Subsequently, environmental emission data above the county scale can be obtained through model transformation and simple calculations. By verifying and analyzing the official data with the one obtained through downscaling at municipal level and above, we verify the feasibility of the models, after which we apply the models to extrapolate information on the industrial waste of the counties of Dongguan city in Guangdong Province, China. We find that the EKCDM should be given priority in most cases, especially for the quadratic parameter model, and that the SM can be adopted when per capita gross domestic product differs greatly between adjacent levels of administrative units. In general, scholars can synthesize the characteristics of these two models, and obtain more accurate data by supplementing and verifying one with the other. Compared with other downscaling methods, our methods require far less data and the concepts are easily understood, which makes them more feasible and increases applicability. This study provides scholars with powerful tools to explore the relationship between industrial pollution and economic development in depth by obtaining industrial waste data at the county scale, thereby supporting scientific research and policy design.

A Copula-based interval linear programming model for water resources allocation under uncertainty.

Water scarcity tends to be aggravated by increase in water demand with the trend of socio-economic development. Thus, non-stationary characteristics of water demand should be identified in water resources allocation (WRA) to alleviate the potential influences from water shortages. In this study, a Copula-based interval linear programming model was established for regional WRA. Through combining correlation analysis and an interval linear programming model, this model can: 1) identify interactions between water demand and socio-economic development levels based on Copula functions, 2) explore variations in water shortage with consideration of multiple risk tolerance levels of decision-makers based on Copula sampling, and 3) obtain desired strategies for WRA through an interval linear programming model. Also, Dalian City in China was selected as a case study area to verify the effectiveness of the model for WRA to five water users (i.e., agricultural sector, industrial sector, public service sector, domestic residents, and ecological environment). Considering multiple tolerance levels of decision-makers to water shortage risk, three scenarios (i.e., S1 to S3), indicating 20%, 40%, and 60% of their low, medium, and high tolerance levels, were proposed. The results showed that the correlation between the amount of water demand and indicators of socio-economic development can be described by Clayton and Gaussian Copula functions. The total water supply of Dalian in 2030 would increase by 2.06%-2.65%, compared with the one in 2025. The allocation of water resources across districts was influenced by varied water demand, energy consumption, and risk tolerance levels. Compared with the amount of water allocation in 2025, the contribution of transferred water sources would increase by 6.71% and 7.04% under S1 and S2 in 2030, respectively, and decrease by 14.31% under S3. With the increase of risk tolerance levels of decision-makers, the amount of water supply in Dalian City would gradually decrease.

Prediction and optimization of regional land-use patterns considering nonpoint-source pollution control under conditions of uncertainty.

Socioeconomic development, leading to significant changes in land-use patterns, has further influenced the output of regional nonpoint-source (NPS) pollution. Multiple uncertainties exist in the processes of land-use changes and NPS pollution export. These uncertainties can deeply affect the management of regional land-use patterns and control of NPS pollution. In this research, an integrated land-use prediction and optimization (ILUPO) model based on system dynamics, export coefficient, interval linear programming, and fuzzy parameter programming models was proposed. The ILUPO model can provide future land-use patterns and NPS pollution loads, and also help optimize the patterns under multiple pollution reduction scenarios. Interval and fuzzy uncertainties in the processes of land-use changes and NPS pollution output can be effectively addressed. The developed model was applied to a water source area in the central part of northern Guangdong Province in South China. For the prediction period 2020-2030 under the high-speed development scenario, results show that cropland area would decrease, while grassland and waterbody areas would increase. In contrast, these three types of land-use would show opposite variation trends under the low-speed development scenario. Construction land area would decrease, while forestland area would increase under both the low-speed and high-speed development scenarios. Variation of the predicted land-use patterns would lead to an increase of total nitrogen loads under each of the scenario, while the total phosphorus loads would show relatively complex variation trends. Regional land-use patterns should be further optimized to mitigate NPS pollution. However, the pollution loads in the study area cannot be reduced by >5% through land-use adjustment. Because cropland would still be the critical source of NPS pollution after optimization, strictly controlling the areas of cropland would be important for the management of such pollution in the research area. In addition, certain areas of grassland and waterbody would need to be converted into cropland and construction land to balance the economic benefit of the system and NPS pollution control. Multiple results obtained from the model under different scenarios of pollution reduction targets and α-cut levels can provide decision-making supports for the local policy makers. The developed ILUPO model can yield insights useful for the planning and adjustment of regional land-use patterns while considering NPS pollution control under conditions of uncertainty.

Optimal configuration of low impact development practices for the management of urban runoff pollution under uncertainty.

The urbanization process has seen an accelerated increase in recent decades, leading to urban runoff pollution becoming more prominent. However, uncertainty of the pollution output and complexity of management systems have made controlling urban runoff pollution challenging. Therefore, it is necessary to propose advanced modeling methods for these challenges. This research presents an integrated urban runoff pollution management (IURPM) model for optimal configuration of low impact development (LID) practices under multiple uncertainties. The IURPM model combines the hybrid land-use prediction and improved pollution estimation models with interval parameter, stochastic parameter, and multi-objective programming. The proposed IURPM model can not only predict the output characteristics, but also provide optimal configuration schemes for the LID practices in the management of urban runoff pollution under multiple scenarios. In addition, uncertainties expressed as discrete intervals and probability density function in the management systems can be effectively addressed. A case study of the IURPM model was conducted in Dongguan City, South China. Results show that considerable amounts of urban runoff pollutants would export from Dongguan City by 2025. The export loads and pollution output flux per unit area would have significant spatial heterogeneity. The results further indicate that population size, gross domestic product, and regional area size are expected to play important roles in the pollution export, while impervious surface coverage and population density would likely have great influences on the output flux of urban runoff pollution. Based on the model findings, multiple LID practices should be adopted in Dongguan City to reduce the urban runoff pollution loads. Using the IURPM model, multiple LID implementation schemes can be obtained under different pollution reduction scenarios and significance levels, that can provide decision-making support for urban water environmental management, considering variations in the policymaker's decision-making preferences. This study demonstrates that the IURPM model can be applied to the optimal configuration of LID practices for the management of urban runoff pollution under uncertainty.

Is the Water System Healthy in Urban Agglomerations? A Perspective from the Water Metabolism Network.

Urban agglomerations are a primary spatial focus of socioeconomic activity and inherently include large volumes of embodied water. We have applied the concept of water metabolism health to comprehensively evaluate the overall operation of water systems in urban agglomerations and propose an innovative assessment framework. In particular, we constructed a water metabolism network (WMN) model to simulate a water system in which different cities and sectors are integrated, combining a newly compiled multiregional input-output (MRIO) table of water flow with ecological network analysis (ENA). A case study considering the Pearl River Delta (PRD) urban agglomeration in 2015 demonstrates that its network is well synergic but highly dependent, with considerable negative effects. Highly developed cities in southeastern of the PRD exhibit higher embodied water productivity and robustness but impose considerable negative effects on the water system. We found the agricultural sector to be a dominant controller of the network; the construction and service sectors represent the primary beneficiaries with strong competition. We suggest measures at various scales to improve water utilization efficiency and promote positive interactions between components, thus improving water metabolism system health for urban agglomerations.

Analysis of urban carbon metabolism characteristics based on provincial input-output tables.

To identify the key contributors of urban carbon emissions as well as the acting paths, it is necessary to analyze the carbon flows from a systematic perspective. Thus, the concept of urban carbon metabolism was introduced in this paper and correspondingly input-output analysis (IOA) and ecological network analysis (ENA) were combined to conduct the carbon metabolism analysis. Concretely speaking, the urban IO table was compiled based on the provincial one and then the direct and embodied urban carbon flows were accounted. Subsequently, the carbon metabolic network model was established, through which the characteristics of the metabolic network were further analyzed to better reveal the contributors and influencing factors of carbon emissions. Dongguan, a city famous as the "factory of the world", was chosen as the case. The results indicate that the total direct and embodied carbon flows were mainly concentrated in manufacture. Manufacture was found to be major factors affecting other compartments through indirect interplay. A trophic hierarchical structure was found, where compartments can be classified into primary producers, secondary producers, primary consumers and secondary consumers according to their metabolic characteristics in use of energy. Electricity, gas & water were defined as secondary producer, and its self-induced carbon flows accounted for more than 95% of the carbon flow conversion within this compartment. By further comparing the metabolic characteristics in Dongguan with that of Guangdong Province and other cities, measures were suggested to heighten energy utilization efficiency and promote positive interactions among compartments to promote the carbon emission reduction in Dongguan.

A simulation-based bi-level multi-objective programming model for watershed water quality management under interval and stochastic uncertainties.

A simulation-based interval stochastic bi-level multi-objective programming (SISBLMOP) model was proposed in this research, through integrating the global nutrient export from watersheds model, interval parameter programming and stochastic chance-constrained programming into a general bi-level multi-objective programming framework. The SISBLMOP model can handle multiple uncertainties expressed as discrete intervals and probability density functions in both the simulation and optimization processes. System complexities, including the hierarchy structure of upper- and lower-level decision makers, can also be addressed in the model. The proposed model is applied to a real-world case study of the Xinfengjiang Reservoir Watershed in South China to identify the satisfactory implementation levels of multiple best management practices (BMPs). The model results show that multiple BMP schemes for water quality management can be obtained under different upper- and lower-level decision-making and risk-violation scenarios, reflecting the cooperation and gaming results of the two-level decision makers. Consequently, the corresponding BMP implementation costs are acceptable to both the upper- and lower-level decision makers. The model is widely applicable and can be effectively used for water quality management under multiple uncertainties and complexities.

Nonzero-Sum Relationships in Mitigating Urban Carbon Emissions: A Dynamic Network Simulation.

The "stove-pipe" way of thinking has been mostly used in mitigating carbon emissions and managing socioeconomics because of its convenience of implementation. However, systems-oriented approaches become imperative in pursuit of an efficient regulation of carbon emissions from systems as complicated as urban systems. The aim of this paper is to establish a dynamic network approach that is capable of assessing the effectiveness of carbon emissions mitigation in a more holistic way. A carbon metabolic network is constructed by modeling the carbon flows between economic sectors and environment. With the network shocked by interventions to the sectoral carbon flows, indirect emissions from the city are accounted for under certain carbon mitigation strategies. The nonzero-sum relationships between sectors and environmental components are identified based on utility analysis, which synthesize the nature of direct and indirect network interactions. The results of the case study of Beijing suggest that the stove-pipe mitigation strategies targeted the economic sectors might be not as efficient as they were expected. A direct cutting in material or energy import to the sectors may result in a rebound in indirect emissions and thus fails to achieve the carbon mitigation goal of the city as a whole. A promising way of foreseeing the dynamic mechanism of emissions is to analyze the nonzero-sum relationships between important urban components. Thinking cities as systems of interactions, the network approach is potentially a strong tool for appraising and filtering mitigation strategies of carbon emissions.

Heterogeneous effects of the availability and spatial configuration of urban green spaces on their cooling effects in China.

The impacts of the availability and spatial configuration of urban green spaces (UGS) on their cooling effects can vary with background climate conditions. However, large-scale studies that assess the potential heterogeneous relationships of UGS availability and spatial configuration with urban thermal environment are still lacking. In this study, we investigated the impacts of UGS availability and spatial configuration on urban land surface temperature (LST) taking 306 cities in China as a case study covering a multi-biome-scale. We first calculated the availability of surrounding UGS for urban built-up pixels in each city using a distance-weighted approach, and its spatial configuration was quantified through the Gini coefficient. Then, we employed various regression models to explore how the impacts of UGS availability and the Gini coefficient on LST varies across different LST quantiles and between day- and nighttime. The results revealed that UGS availability was negatively associated with both daytime and nighttime LST, while the Gini coefficient showed a positive impact solely on daytime LST, indicating that an adequate and equally distributed UGS contributes to lower environmental temperatures during the daytime. Furthermore, the impact of UGS availability on LST decreased during both day- and nighttime with increased background LST quantiles. Whereas the impact of the Gini coefficient increased only with daytime LST quantile levels, with its effect remaining almost insignificant during the night. Our findings provide new insights into the impacts of UGS on urban thermal environment, offering significant implications for urban green infrastructure planning aiming at lowering the urban heat island.

Downscaling of environmental indicators: A review.

Environmental indicators at different scales are important for environmental management, daily life, and scientific research. Because of the lack of statistics below a national scale for many environmental indicators, scholars have developed various downscaling methods to obtain finer-scale and diverse forms of data for different environmental indicators. However, the existing downscaling methods for environmental indicators are diverse and fragmented. Here, we reviewed the downscaling methods by reclassifying the environmental indicators from a life cycle perspective into five categories: natural resources use and related attributes; material and energy consumption; environmental discharge; climate change; and environmental footprints. We first provide a general introduction to downscaling theory in the environmental field, including definitions, techniques, and evolution. We then elaborate on downscaling methods and make an inventory of the five categories of environmental indicators. We summarize the downscaling methods commonly applied to specific indicators, scale transformation, the strengths and limitations of corresponding methods, and provide specific examples. Next, we discuss ways to select or construct downscaling methods based on four principles: objective orientation, data accessibility, model feasibility, and model adjustment. Finally, we explore the future direction of downscaling and provide insights for improving downscaling for environmental indicators. In this review, we generalize and clarify the downscaling techniques for environmental indicators, which will help facilitate the appropriate selection of downscaling methods by researchers.

Greenhouse gas inventory of a typical high-end industrial park in China.

Global climate change caused by greenhouse gas (GHG) emissions, which severely limits the development of human society and threatens the survival of humanity, has drawn the international community's long-term attention. Gathering the most important production factors in the region, an industrial park usually represents the development level of specific industries in the region. Therefore, the industrial park should be regarded as the base unit for developing a low-carbon economy and reducing GHG emissions. Focusing on a typical high-end industrial park in Beijing, we analyze the carbon sources within the system boundary and probe into the emission structure in view of life-cycle analysis. A GHG inventory is thereby set up to calculate all GHG emissions from the concerned park. Based on the results, suggestions are presented to guide the low-carbon development of the high-end industrial park.

The efficient utilization of thiocyanate on simultaneous removal of ammonium and nitrate through thiosulfate-driven autotrophic denitrifiers and anammox.

The efficient utilization of thiocyanate remain be an important bottleneck in the low-cost nitrogen removal for wastewaters containing thiocyanate. The study aimed to investigate the feasibility of thiocyanate in removal of nitrate and ammonium through anammox (AN) and thiosulfate-driven autotrophic denitrifiers (TSAD). The results showed that removal of nitrate and ammonium were achieved rapidly utilizing thiocyanate, which was attributed to degradation of thiocyanate by TSAD and cooperation with AN. The utilization efficiency of thiocyanate in nitrogen removal was increased by 250% due to the microbial cooperation. Excess thiocyanate and ammonium did not influence the nitrogen removal amount. However, the nitrogen removal were affected obviously by the biomass ratio (XAN/XTSAD) between AN and TSAD Moreover, the dynamics related to removal of pollutants was described successfully by a modified Monod model with time constraints. These findings offer an insight for efficient utilization of thiocyanate in nitrogen removal via microbial cooperation.

Material flow analysis of fossil fuels in China during 2000-2010.

Since the relationship between the supply and demand of fossil fuels is on edge in the long run, the contradiction between the economic growth and limited resources will hinder the sustainable development of the Chinese society. This paper aims to analyze the input of fossil fuels in China during 2000-2010 via the material flow analysis (MFA) that takes hidden flows into account. With coal, oil, and natural gas quantified by MFA, three indexes, consumption and supply ratio (C/S ratio), resource consumption intensity (RCI), and fossil fuels productivity (FFP), are proposed to reflect the interactions between population, GDP, and fossil fuels. The results indicated that in the past 11 years, China's requirement for fossil fuels has been increasing continuously because of the growing mine productivity in domestic areas, which also leads to a single energy consumption structure as well as excessive dependence on the domestic exploitation. It is advisable to control the fossil fuels consumption by energy recycling and new energy facilities' popularization in order to lead a sustainable access to nonrenewable resources and decrease the soaring carbon emissions.

Greenhouse gas emission accounting and management of low-carbon community.

As the major source of greenhouse gas (GHG) emission, cities have been under tremendous pressure of energy conservation and emission reduction for decades. Community is the main unit of urban housing, public facilities, transportation, and other properties of city's land use. The construction of low-carbon community is an important pathway to realize carbon emission mitigation in the context of rapid urbanization. Therefore, an efficient carbon accounting framework should be proposed for CO2 emissions mitigation at a subcity level. Based on life-cycle analysis (LCA), a three-tier accounting framework for the carbon emissions of the community is put forward, including emissions from direct fossil fuel combustion, purchased energy (electricity, heat, and water), and supply chain emissions embodied in the consumption of goods. By compiling a detailed CO2 emission inventory, the magnitude of carbon emissions and the mitigation potential in a typical high-quality community in Beijing are quantified within the accounting framework proposed. Results show that emissions from supply chain emissions embodied in the consumption of goods cannot be ignored. Specific suggestions are also provided for the urban decision makers to achieve the optimal resource allocation and further promotion of low-carbon communities.

CO2 mitigation measures of power sector and its integrated optimization in China.

Power sector is responsible for about 40% of the total CO2 emissions in the world and plays a leading role in climate change mitigation. In this study, measures that lower CO2 emissions from the supply side, demand side, and power grid are discussed, based on which, an integrated optimization model of CO2 mitigation (IOCM) is proposed. Virtual energy, referring to energy saving capacity in both demand side and the power grid, together with conventional energy in supply side, is unified planning for IOCM. Consequently, the optimal plan of energy distribution, considering both economic benefits and mitigation benefits, is figured out through the application of IOCM. The results indicate that development of demand side management (DSM) and smart grid can make great contributions to CO2 mitigation of power sector in China by reducing the CO2 emissions by 10.02% and 12.59%, respectively, in 2015, and in 2020.