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One of the impending consequences of the policy-driven, rapid penetration of electric vehicles is that a substantial amount of expired EV batteries will present a waste collection and management problem, particularly in the urban context. Motivated by a lack of research on this issue, this paper evaluates the relative benefits of shared versus non-shared collection systems in supply chains where the service outlets are not exclusive to particular automakers. Using a mixed-integer optimization model, the analysis features spatiotemporal and multiple stakeholder complexities. Data are based on a large- scale representative case study from Beijing. The results show that a shared collection service system contributes to higher profitability, higher collection rates, increased environmental benefits and improved facility utilizations. As a consequence, this research contributes to the policy of supply chain liberalization to order to foster the efficient waste management of EV batteries.

Carbon capture, utilization, and storage (CCUS) is promising for carbon neutrality vision in steel industry. Previous work has overestimated both mitigation potentials and economic costs, hindering the long-term planning of CCUS. Here, we propose a soft-linking approach, combining the industry decarbonization pathway and emission source-sink matching models, to avoid both overestimations in China’s steel industry. Results show the emissions of China’s steel industry are estimated to decrease by 38.9-55.2% under three scenarios before retrofitted by CCUS. The optimized CCUS layouts have mitigation effects of 438.01-529.02 Mt, meeting the requirements of the industrial –level decarbonization pathways. The specific costs of CCUS are 138-150 CNY/t, 29-65% lower than the unselected sources. CCUS will increase the energy and water consumption by 25%+ with uneven distribution, exacerbating local resource scarcity problems. The methodology and findings can clarify the mitigation effects and costs of the CCUS technology to set proper long-term planning for its promotion.

To implement strict environmental targets in China's cement industry into small regions, one should evaluate the city-level eco-efficiency that provides comprehensive instruction. This study establishes a plant-level database with 4000+ production lines located in 341 cities, calculates the energy consumption and CO

Evaluating water quality characteristics (WQC) and tracing pollutant sources (PS) have gradually attracted worldwide attention. This study was conducted to develop an integrated method framework for evaluating WQC, tracing PS, and improving understanding of their relationship to efficiently managing the water environment. The single-factor index, comprehensive water quality index (CWQI), and hazard quotient and hazard index (HQ and HI) were used to evaluate the characteristics of single pollutant concentration, comprehensive concentration, and human health risk, respectively. These evaluation methods combined with relevant standards selected data from the original sampling data. These selected data were used for tracing PS by principal component analysis and Pearson correlation methods. 3384 sampling data were collected in the Yellow River Basin in 2021, and the WQC assessment and pollutant traceability were carried out by using the above-integrated method framework. The results showed that TN(total nitrogen) was the primary pollutant with an average concentration of 4.54 mg/L, followed by CODcr(dichromate oxidizability), NH

In order to further explore the internal transmission mechanism between technological innovation and green development in manufacturing industry under the background of obvious development characteristics in the new era, this paper constructed an integrated methodology system to evaluate the internal impact mechanism of technological innovation value chain efficiency on green development efficiency based on spatial perspective. First, the Network Slack-based model and Global Malmquist-Luenberger model are constructed to reveal the internal development law of technological innovation and green development of manufacturing industry. Secondly, the spatial Dubin model is employed to analyze the impact of current development characteristics and technological innovation on green development. The results show that innovation value chain efficiency is higher than technological innovation efficiency, and economic transformation efficiency is lower than that of technological innovation value chain. During the study period, the efficiency of technological innovation value chain in the four economic regions present fluctuant growth trend, and the eastern region has the highest value. The green development efficiency in the east, central, west, and northeast regions of manufacturing industry is higher than 1, and it shows an obvious spatial agglomeration effect. Besides, the efficiency of technological innovation, information and communication technology, urbanization, and the advanced industrial structure are all conducive to the improvement of green development in manufacturing industry. This paper studies the influence mechanism of technological innovation value chain efficiency on green development based on spatial perspective and puts forward relevant countermeasures and suggestions to effectively promote green development of manufacturing industry, providing relevant theoretical research for green and high-quality development.

For urban agglomerations in the bay area, which concentrate multiple environmental elements and intense anthropogenic activities, comprehensive control of nitrogen pollution is particularly challenging due to diverse cross-media migration and transformation forms of nitrogen pollutants. Existing studies on urban nitrogen metabolism mainly focused on quantification of nitrogen flux, without systematic consideration of physiochemical changes of nitrogen between environmental media. This study conducted a dynamic simulation of nitrogen cross-media metabolism in urban agglomeration over 30 consecutive years, and recognized the types, quantities, and trends of cross-media transfer of nitrogen pollution as well as pollution control paths based on ecological network analysis and scenario analysis. Taking the Guangdong-Hong Kong-Macao Greater Bay Area as the case, results show that during its fast-urbanized stage in 1989-2018, more than 25% of the total nitrogen pollution emissions were transferred from other media. The higher degree of imbalance between the socioeconomic system and the soil in the nitrogen metabolic network emphasizes the increased pressure and necessity of pollution control of nitrogen in the solid state with urban development. Promoting fertilizer reduction and sludge land use are priority paths for collaborative control of cross-media nitrogen pollution. The study provides methods to systematically analyze the features of cross-media transfer of nitrogen pollution at the city level, and accordingly propose paths aiming at sustainable urban nitrogen management with multi-media integrity and synergy.

Urban biowaste is the organic fraction of municipal solid waste (MSW) and is a predominant waste type in low- and middle-income countries. Urban biowaste is the main cause of pollution and produces odor and leachate, and it could also serve as a source of energy and nutrient elements. Therefore, urban biowaste management should actuate minimal pollution, maximized resource utilization, and economic feasibility, which makes it a multi-objective problem. With increasing requirements for the classified management of MSW, the complexity of urban biowaste management is increasing, and it is necessary to consider the synergy mechanism between different wastes and technologies from a systematic perspective. We constructed urban biowaste management integrated model (UBMIM) to support urban biowaste management system design and policy formation. Firstly, a dynamic quantitative simulation of the numerical matching and influence conduction was conducted based on technology system synergy mechanism. Secondly, a multi-objective evaluation of the technology system was conducted based on material flow analysis, life cycle assessment, and project economic benefit assessment. On this basis, a multi-objective optimization algorithm was used for technology selection under high-dimensional objectives, and the long-term risks were identified and policy recommendations were made based on an uncertainty analysis algorithm. As a case study for the application research of the model, Suzhou, China, was selected, and integrated technology solutions and policy suggestions were provided for 2020 and 2025. The optimized solution can improve the system's efficiency of energy-saving and emission reduction by 14.5%-400.9% while reducing operating costs and new investments.

The rapidly growing output of solid waste has brought tremendous pressure to urban development. China launched an action plan known as “Zero-waste city” (ZWC), that refers to an urban development model aimed at reducing the generation of and enhancing the recycling of solid waste, in order to alleviate environmental impacts. Eleven cities and five special zones achieved positive results of solid waste management were selected as pilot areas for exploring empirical methods until 2019. The practices and lessons of the pilot cities need to be deeply analyzed and summarized, so as to promote successful models, learn lessons and better implement the policy comprehensively for other cities. This study presents a review of China’s ZWC policies and practices with constructive suggestions for further development. Based on the policy objective of ZWC and the field investigation of solid waste flow, five crucial approaches to developing ZWC are proposed, namely, solid waste reduction throughout the industrial chain, collaborative treatment of classified municipal solid waste (MSW), efficient utilization of agricultural waste with multiple purposes, safety control of hazardous waste flow, and optimization of market mechanisms. The case study demonstrates that the five paths are appropriate to Xuzhou city. However, deficiencies in MSW classification, pesticide packaging waste collection systems, solid waste product application, management, and policymaking, have emerged. Regulatory prohibitions, extended producer responsibility and market vitality should be adopted to improve the collection, transportation, and utilization of solid waste. Key findings from this research are to summarize crucial paths toward fulfiling ZWC goal, and to reveal some successful practices of, and lessons from ZWC construction by case studies. This study provides a method to further implement zero solid waste management in a targeted manner. The recommendations drawn from the study, which include law, market and institutional measures, may contribute to the achievement of developing sustainable cities.

Ecological environment conditions (EEC) assessment plays an important role in watershed management. However, due to insufficient field data, EEC assessment in large-scale watersheds faces challenges. Our study was conducted to develop an effective EEC assessment method framework that was capable of reducing the use of field data. Three indicators were developed from multisource data, including landscape ecological risk index (LERI), road network density (RND), and industry density (ID). The knowledge-based raster mapping approach integrated the three indicators into an overall score of the EEC. Then model validation was conducted with principal components of water quality from field sampling data by Pearson correlation analysis methods. Finally, we applied and demonstrated the constructed method framework in the EEC assessment of the YRB.The results showed that bad EEC (0.5326Overall score ≤ 0.7679) areas were mainly distributed in the northern part of the YRB, showing a circular distribution pattern. The areas with bad EEC were 15.84 million km

From the start of China’s G20 presidency, China positions itself as a world leader in fighting climate change and emphasizes the wish to ‘break a new path for growth’. China aims to peak carbon dioxide (CO2) emissions by 2030 and cut its greenhouse gas emissions per unit of gross domestic product by 60–65% from 2005 levels by 2030. The pledge is eagerly awaited as China aims to develop a low carbon economy through switching to alternatives to fossil fuels and being technologically energy-efficient. The power industry is the most important industrial sector while the biggest bottleneck for CO2 emission control in China. This paper develops a technologies-based bottom-up CO2 mitigation model to assess emission reduction potential of different technologies in the thermal power industry up to 2030. Using 2010 as the reference year, two macro-economic scenarios and four technological scenarios have been set to describe future policy measures for the period of 2015–2030. CO2 emission trends, reduction potentials and cost curves are demonstrated under different scenarios. The results show that the electric power industry can reach its CO2 emission peak by 2030 in the middle policy control scenario under macro-economic slow growth. Emissions would peak at 4.6 billion tonnes CO2-eq for the least cost scenario, which is 1.78 billion tones CO2-eq less than peak the BAU scenario in 2030. This is equivalent to the total CO2 emissions from 300 MW to 1000 MW coal-fired power plants with 5000 h in 30 provinces and municipalities of China in 2013. This research shows that the top four negative cost-beneficial technology options, 630℃ or 700℃ USC, small hydroelectricity, and nuclear power pressurized water reactor II and III, are the most preferable to be promoted to meet the CO2 emissions peak target in 2020 and 2030.

The building sector has become the main source of implicit greenhouse gas(GHG)emissions worldwide, and Macau is no exception. However, as yet, there is a lack of work to quantify the GHG emissions and guide cities on how best to reduce emissions. By using the life cycle assessment method, this study is therefore designed to estimate the GHG emissions from urban buildings during the period of 1999–2016, to figure out the major contributors, and finally, to propose potential effective measures for GHG emissions reduction in Macau. The results show that total GHG emissions from buildings have grown quickly from 1.52 million metric tons (Mt) CO2e in 1999 to 5.91 Mt CO2e in 2016. The use phase contributes to most GHG emissions during the whole life stage, accounting for 65.75% of the total GHG emissions, followed by construction stage (33.72%). The unit GHG emissions in the construction stage is 1.47 (0.84–3.82) tons CO2e/m2, while for the use stage, unit GHG emissions from resident and non-resident buildings in 2016 are up to 65.33 and 3316.37 CO2e/m2/year, respectively. It is noted that EoL stage has ‘negative value’, indicating that it could generally bring some environmental benefits due to materials recycling. e.g. In 2016, the environmental impacts and benefits in the EoL stage are 21.12 and 85.94 Kt CO2e, respectively. The gaming industry and clean electricity should be the key points of building GHG emission reduction in Macau. Further scenario analysis shows that the enforceable policies can slow down the growth rate of GHG emissions.

The energy conversion efficiency of coal utilization is a focal point in modern energy management practices and in academic literature. This paper explores life cycle energy efficiency of coal (LEEC), which encompasses six distinct stages from the design of coal mines to the comprehensive reuse of resources. First, the study analyzes improvements to the evaluation of efficiencies in energy systems. Secondly, it proposes a multi-stage energy efficiency analysis and constructs an innovative LEEC model for coal exploitation and utilization. Thirdly, the energy efficiency of coal exploitation and utilization was evaluated, and found that total efficiency in China increased from 33.11% in 2010 to 37.69% in 2014, saving 126 Mtce of energy. Finally, this study found that mine design is the most important stage in improving energy efficiency, followed by the coal processing stage. Moreover, we found that coal processing has an indirect contribution to other stages in the life cycle utilization of coal, such as reducing transportation capacity. The primary research contribution is the development of a comprehensive LEEC model for the entire life cycle of coal, rather than just single-stage evaluation. This lays the foundation for further studies on energy efficiency of industrial chains and across entire systems.

The search for appropriate models for predictive analytics is currently a high priority to optimize anaerobic fermentation processes in industrial-scale biogas facilities; operational productivity could be enhanced if project operators used the latest tools in machine learning to inform decision-making. The objective of this study is to enhance biogas production in industrial facilities by designing a graphical user interface to machine learning models capable of predicting biogas output given a set of waste inputs. The methodology involved applying predictive algorithms to daily production data from two major Chinese biogas facilities in order to understand the most important inputs affecting biogas production. The machine learning models used included logistic regression, support vector machine, random forest, extreme gradient boosting, and k-nearest neighbors regression. The models were tuned and cross-validated for optimal accuracy. Our results showed that: (1) the KNN model had the highest model accuracy for the Hainan biogas facility, with an 87% accuracy on the test set; (2) municipal fecal residue, kitchen food waste, percolate, and chicken litter were inputs that maximized biogas production; (3) an online web-tool based on the machine learning models was developed to enhance the analytical capabilities of biogas project operators; (4) an online waste resource mapping tool was also developed for macro-level project location planning. This research has wide implications for biogas project operators seeking to enhance facility performance by incorporating machine learning into the analytical pipeline.

Under the restriction of multiple industrial environmental targets, the difficulty of industrial environmental management, as a many-objective optimization problem, has increased significantly. As traditional optimization methods such as bottom-up models and commonly used intelligent algorithms have drawbacks in solving many-objective optimization problems, we introduce the third edition of Non-dominated Sorting Genetic Algorithm (NSGA-III) to the environmental management problem in China’s iron and steel industry. We build a many-objective optimization model to plan the application of the four types of decision variables: process equipment, cleaner production technologies, end-of-pipe treatment technologies and synergic technologies. In total, 7 objectives including the minimization of energy consumption, 5 types of pollutant reduction and economic cost are considered. In addition, to formulate final decision schemes, we adopt the Fuzzy C-means Clustering Algorithm to cluster the Pareto-optimal solutions. The results show that NSGA-III performs well in center distance, spacing metric, and computational efficiency. The Pareto-optimal solutions reflect that SO2 reduction target, is too strict, while others, such as energy conservation and PM emission reduction are too loose. Besides, we obtain four final decision schemes based on different objective preferences. In sum, the proposed methodology is proved to be capable of solving many-objective optimization problems and helping decision making in industrial environmental management.

Currently, over 20% of global electricity and 30% of global CO2 emissions from fuel combustion are generated in China. Understanding the driving forces of CO2 emissions from power generation is critical for both decarbonizing the power sector and achieving national carbon reduction targets. The objectives of this study were to identify critical driving forces behind changes in CO2 emissions from the power sector and to propose appropriate decarbonization pathways. First, the generation and demand structures of the power sector were introduced into a CO2 emission accounting model and decomposition analysis. Instead of traditional input–output analysis, structural decomposition analysis modified using a power transmission table was used to investigate the impacts of five driving factors of CO2 emissions from China’s power generation. The five driving factors comprised the proportion of thermal power, power generation technology, power generation structure, power demand structure and power demand, whereby the latter was divided further into nine detailed parts. Considering five regional power grids in China, the contributions of these factors were analyzed at both national and regional level. The results showed that the majority of the increase in CO2 emissions during 2007–2012 could be attributed to electricity generation (96.2%) driven by changes in power demand, which should be the key to power sector decarbonization. By contrast, 30.7% of emissions were offset by changes in the proportion of thermal power and technology, demonstrating the obvious effects of China’s policy on clean energy transition. Additionally, all power grids exhibited an increase in CO2 emissions from electricity generation, with the east and central grids accounting for 64% of the national increase. Power transmission structure had only a small impact on CO2 emissions from power generation. By using the electricity transmission table, we modified SDA to overcome the time lag issues and eliminate the reliance on IO data, and continuous annual data rather than aggregated five-yearly data can be used to capture the structural effects, thus providing more precise results for the driving forces of emission changes. Our case study shows that there is huge potential in extending the IO-based SDA method to other trade-related studies.

Municipal solid waste (MSW) landfills are among the largest anthropogenic sources of methane. However, there are also potential sources of landfill gas (LFG), which can be used to produce energy products, meaning that the recycling of LFG has gained global attention. Scientific evaluation of spatio-temporal LFG recycling potential is necessary to accelerate the promotion of LFG utilization. However, for a big country with uneven regional development level like China, the amount of LFG that can be recycled and utilized is difficult to determine, and there has been limited research that considers variation in regional characteristics, landfill sites and technologies. Therefore, this research first used sub-regional parameter sets that embodied the MSW characteristics of different regions to calculate LFG generation in China from 1990 to 2020. Next, this research estimated the LFG recovery potential for different regions in 2020 based on analysis of LFG collection and utilization technology, including technical parameters and application in different landfill sizes. The results showed that LFG generation in China will reach peak value around 2019 (3.49 billion N m3 of methane), and decrease quickly after that. The quantity of LFG with the utilization condition in 2020 will be 3.30 billion N m3, which could produce 7.39 billion kW h of electricity or 1.70 billion N m3 of bio natural gas. This could substitute 85.5% of electricity consumption or 25.3% of natural gas consumption in the transportation sector in 2014 (8.63 billion kW h and 6.74 billion N m3 respectively). Moreover, the findings show that the peak in LFG generation and a massive closure period of Chinese landfills will coincide. This will provide an opportunity to promote LFG utilization during the closed field management of landfills. Overall, LFG recycling presents a strong opportunity, and is in urgent need of promotion policies.

Industrial symbiosis (IS) uses a metaphor drawn from the natural ecosystem that ‘nothing is being wasted in nature’ to suggest that one factory can use another factory's waste as its feedstock. Through qualitative research and thematic analysis, this study has identified three themes for fostering IS: IS fostering models, IS implementation stages, and policy instruments. A framework for studying the influence of policy on IS implementation from the firm's perspective is proposed, using four IS fostering models, five IS implementation stages, and ten policy instruments. We demonstrate that studying IS through these combined lenses brings many new detailed insights. Following this, the use of the framework to conduct comparative case studies to consider which policy instrument worked well under which conditions was demonstrated using the case of the Speed-dating/exogenous IS model between the UK and China. Accordingly, this framework is expected to contribute to efficient policy support, which will increase the proliferation of IS practice and the potential of IS practicality.

Collection is a key activity in sustainable solid waste management and resource recycling. In many developing countries, collection is undertaken mainly by the informal sector. This is accompanied by various environmental, social, health and efficiency problems. Some top-down experiments to integrate informal collection into the waste resource recycling chain have proved unsuccessful. Meanwhile, Internet and Communication Technologies (ICTs) in waste management are formulating a new collection model: intelligent collection. In China, there are dozens of emerging companies who are engaging in intelligent collection of recyclables. What are the intelligent collection cases in China? Do they have potential to integrate the informal collection into the waste recycling chain? To answer these questions, we selected and interviewed 15 Chinese intelligent collection companies to identify their organisational model and comparative advantages over informal collection. We found that intelligent collection companies in China operated in two forms: human-human interaction collection and human–machine interaction collection. Comparative advantages were found in organisation, trade, data accumulation, and profit making sources. These render them with a high potential to integrate informal collection. Intelligent collection in China is still at an early stage. Its potential for a sustainable business model needs to be further explored. Its application as a supplement to the Municipal Solid Waste collection system and as an exclusive collection for high value waste items under the Extended Producer Responsibility framework seems promising.

Residents’ participation in classification and recycling of urban household solid waste (HSW) is a critical factor for the success of municipal solid waste management. The aim of this study is to investigate the decision-making mechanism of residents’ HSW disposal behaviors by merging the theory of planned behavior and the Attitude-Behavior-Condition theory. In this study, based on the survey data of 709 residents in Suzhou, China and structural equation modeling method, the main factors that affect residents’ HSW disposal behaviors and their degree of influence were analyzed, followed by discussion on decision-making mechanisms. The findings show that residents’ behavioral selection has been significantly related to four intrinsic subjective factors and seven external objective factors, and the combined effect of the latter ones is nearly twice of that of the former ones. Moreover, the convenient of environmental facilities and services are most effective at promoting residents’ participation in HSW classification and recycling. Specifically, the observed variables of publicity and education, accessibility to recycling facilities, accessibility to classification facilities, willingness to participation of classification and residents' environmental awareness are the five most significant factors. The impact of laws and regulations is not significant; however, this may be because that there was no mandatory laws, regulations and incentive mechanisms on HSW classification and recycling in Suzhou in this period, and there is still a big gap and room for improvement in this aspect in mainland China. Finally, the study put forward relevant policy recommendations for the comprehensive management of urban HSW classification and recycling.

With the rapid development of the express delivery industry, the environmental issues of express packaging waste (EPW) have gradually attracted public attention worldwide. As important participants, consumers' behaviors and attitudes will play a vital role in solving the problem. This study uses the conditional value method (CVM) to evaluate the differences in the attitudes and willingness to pay of urban and rural residents toward EPW in Guangdong Province, China. The results show that the respondents have limited knowledge of the recycling situation, relevant policies and environmental impacts of express delivery packaging, although more than 60% of respondents do think that there is a problem with excessive packaging. The low recycling rate for packaging materials is mainly attributed to the lack of recycling facilities and publicity on environmental issues. The differences between urban and rural residents are mainly over disposal methods, the views on excessive packaging and willingness to pay (WTP) for EPW. Finally, the WTP values of urban and rural residents choosing a “deposit” system are 1.58 ($0.24) and 1.79 yuan ($0.28) per piece, respectively, while the WTP values (for increased fees) are 0.64 yuan ($0.10) and 0.60 yuan ($0.09) per piece, respectively. The obtained results may serve as a reference for different regional responses to the promotion and improvement of EPW management in the future.

The report of the 19th National Congress of the CPC stated that building an ecological civilization is vital to sustain the Chinese nation’s development and is the only way to address the contradiction between people’s ever-growing needs for a good ecological environment and the inadequate effective supply of ecological products. At the National Conference on Eco-environmental Protection in 2018, the Xi Jinping Thought on Ecological Civilization was established as a beacon for developing ecological civilization. Revolving around the major requirements of this campaign, universities, as institutions undertaking scientific research and talent cultivation, should on the one hand lead as pioneers and guides in promoting green, circular and low-carbon development in the endeavor to contribute to China’s and the world’s sustainable development. On the other hand, they should also fully exert their advantages and roles to actively advance education in ecological civilization and explore the path of building green universities, and advocate energy conservation and emission reduction on campus, as part of the efforts to promote regional environmental improvement.

Whilst the widespread adoption of electric vans is necessary to improve urban air quality and reduce carbon emissions, it is also self-evident that adequate charging stations are a precondition. However, the investment case for basic charging stations without public subsidies is challenging. In the context of a London case study, four business models are compared, which integrate solar power generation and new/second-life battery storage system with the basic charging facilities. Considering the uncertainties of electricity tariff and solar generation, the optimal infrastructure investment and operational planning has been formulated as a two-stage stochastic optimization model. The results show that: (i) in the integrated business models, the return on investment and charger installations could be increased by up to 5.39% and 17.06% respectively, and the carbon intensity could be reduced by up to 8.13%; (ii) the nondiscriminatory grant annualized as 50 £ is not sufficient, and a differentiated government subsidy policy may be more conducive to achieving a positive return on investment, such as 50 £ for fast chargers and 100 £ for rapid chargers; (iii) in the integrated business models, fast chargers undertake more vehicle-to-grid electricity exchange with the pattern adoption rate increased by up to 52.38%, while rapid chargers mainly ensure the timely charging completion with the usage frequency increased by up to 2.82%.

Industrial-environmental management is a multi-objective optimization problem plagued with multiple uncertainties. Most studies only optimize few objectives and often neglect these uncertainties. This study builds a 6-objective optimization problem to quantify energy conservation and emission reduction (ECER) potentials in China's iron and steel industry. First, uncertainties are simulated through 100,000-time random sampling, NSGA-II and the mean-effective objective mechanism are applied to calculate optimal solutions. Finally, a global sensitivity analysis is performed to classify uncertainty parameters based on their impacts on objectives' performance. Results show: (1) There exist significant discrepancies between objectives' performance under certainty and uncertainty. For example, the deterministic CO

Since the late 1990s, the trend of plastic waste shipment from developed to developing countries has been increasing. In 2017, China announced an unprecedented ban on its import of most plastic waste, resulting in a sharp decline in global plastic waste trade flow and changes in the treatment structure of countries, whose impacts on global environmental sustainability are enormous but yet unexamined. Here, through the life cycle assessment (LCA) method, we quantified the environmental impacts of changes in the flow patterns and treatment methods of 6 types of plastic waste in 18 countries subsequent to the ban. In the short term, the ban significantly improved four midpoint indicators of environmental impact, albeit contributed to global warming. An annual saving of about 2.35 billion euros of eco-cost was realized, which is equivalent to 56% of plastic waste global trade value in 2017. To achieve global environmental sustainability in the long run, countries should gradually realize the transition from export to domestic management, and from landfill to recycling, which would realize eco-costs savings of about 1.54–3.20 billion euros., China announced a ban on its import of most plastic waste in 2017, resulting in an impact on global environmental sustainability. Here the authors quantify the environmental impacts of changes in the flow patterns and treatment methods of 6 types of plastic waste in 18 countries subsequent to the ban.

The cement industry emits 8% of global CO2 emissions. Representing over 50% of these emissions, China's Cement Industry (CCI) will play a key role towards achieving CO2 emission peak and Carbon neutrality. This study designed a bottom-up Green Transition Roadmap model to assess the feasibility of China's newly proposed goal – achieve Carbon neutrality by 2060. Six emission abatement measures are explored under three scenarios based on green transition policies. Random sampling of uncertainty factors is also performed to investigate their effects on decarbonization roadmaps. Finally, GAINS model and abatement cost curves are used to monetize decarbonization co-benefits. Results show that: (1) Achieving Carbon neutrality in CCI by 2060 is ambitious but possible under advanced and aggressive abatement scenarios, with a net cumulative cost of 279–345 billion CNY after deducing monetized co-benefits; (2) Uncertainties significantly upset decarbonization roadmapsas it takes an additional 4–6 years to achieve the same emission reductions compared to deterministic scenarios; (3) Circular economy should be prioritized in the roadmap since it is least costly and contributes 33.6% of CO2 emission abatement; (4) From 52-technologies assessed, 10-technologies account for 75% of abatement potential brought by advanced efficiency technology measure. Most are calcination process technologies, reflecting the urgency to target this emission intensive process; (5) Realizing Carbon neutrality brings huge co-benefits in energy saving and pollutant reduction. Specifically, fossil energy, PM, NOx and SO2 intensities drop by 65%, 48%, 64% and 92% in 2060 respectively. Therefore, policy-makers can resort to deep decarbonization to reach stringent air pollution standards.

The environmental impact, energy conservation, and economic cost are prominent decision criteria in municipal solid waste (MSW) management, among which trade-off relationships widely exist because of different features of pollutant treatment technologies. These three objectives should thereby be simultaneously considered in the design of technology combinations in MSW treatment system (MSWTS). In addition, comprehensive characterization of environmental impact of the whole MSWTS should cover the complex pollutants cross-media metabolism in the treatment of both MSW and subsequent secondary pollution. This study developed a multi-objective optimization model to select optimal technology solutions in MSWTS. Three objectives, the minimizations of total environmental impact calculated from pollutants cross-media metabolism perspective, net energy consumption, and total cost are optimized through the second generation of the Non-dominated Sorting Genetic Algorithm (NSGA-II). Final MSW management schemes under environment, energy, and cost preferences are obtained through Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. This paper uses China's MSWTS as a case study and finds that Pareto optimal solutions can reduce the total environmental impact and the net energy consumption by 24.2% and 7.4% respectively, while increase the total cost by 18.2% in average, compared with the baseline scenario. The promotion of MSW biological treatment technologies, especially anaerobic digestion (AD), can effectively improve the environmental performance of MSWTS, while the current vigorous promotion of MSW incineration in China is not recommended. Sludge co-processing in cement kiln is highly promoted under all three types of management preferences. In summary, the proposed methodology can provide decision support for the optimal design of technology solutions in MSWTS.

The takeaway food industry, involving more than 0.4 billion consumers in China, has brought mass of packaging waste and salient environmental burden. Here we mapped the distribution of takeaway food industry across China including the industry scale, diet structure and order time based on the analysis of more than 35 million takeaway food orders. The real use situation of various packaging materials in the takeaway food industry market has been clarified. The life cycle assessment of “a piece of takeaway food delivery order” has been carried out in different regions. Results show that in addition to plastic waste generation, takeaway food industry causes more types of environmental impacts. In terms of the national resource consumption, greenhouse gases emission, water pollution and health damage risk, the top 5 ranked provinces in each accounted for 44%, 48%, 43% and 49%, respectively. Under the latest Chinese plastic pollution control policy, the industry needs to reduce 1.12 million tons of non-degradable plastic packaging by the end of 2025, and 65% of the pressure is clustered in the metropolis and provincial capitals. However, without targeted and regionally differentiated plastic pollution control policies, the environmental impact control of takeaway food industry is still ineffective. It is urgent to explore the control measures applicable to different regions. Overall, packaging reduction is more effective than material substitution.

Carbon footprints are usually more conducive to doing comparative analysis or carbon management between different enterprises or different countries. Regarding how to accurately estimate the carbon footprint per kWh of coal-fired electricity, different studies can give different answers since there are no uniform system boundaries (e.g. the direct and indirect emission) or data sources, which hinders the carbon reduction management of the coal-fired power industry. Addressing this issue would improve the level of carbon management and carbon control in the coal-electricity industry. This paper develops a life cycle accounting model to analyze the carbon emissions of China's coal-fired power generation plants. We calculated the direct and embodied carbon emission of CO2 and CH4 from carbon emission and carbon reduction. Results reveal that the life cycle coal-fired carbon emissions are in line with the trend of coal consumption and electricity generation, and carbon emission of the life cycle is higher than direct emission of coal-fired power generation by 10%–13%, resulting in a net increase of 332 MtCO2e in 2016. Based on the systematic uncertainty analysis, we believe the estimates could be more accurate than the past researches. Finally, the paper provides three tiered approaches to improve the carbon management.