Sustainable Production and Consumption最新文献

Water conservation measures are a means of adapting to the increasing pressures on water supply in cities. However, its widespread adoption requires knowledge of its implications at the different levels of the urban water cycle. This paper aims to provide a holistic overview and global understanding of the opportunities, challenges and implications associated with the widespread adoption of water conservation measures on the urban water cycle. A systematic search was conducted to identify water conservation case studies within the municipal sector. The analysis of studies' results highlighted that water conservation can significantly reduce buildings' water use while also providing energy savings in both buildings and water utilities. Water conservation measures were found to be economically feasible in most cases, with relatively short payback periods, although conclusions were more nuanced when adopting alternative water sources. This suggests that water conservation measures may be a better option than supply-side solutions to maintain water supply in cities. However, their performance may be undermined by design problems or user behaviour. Therefore, education and communication are key drivers for their widespread adoption. Lower water demand resulting from the implementation of conservation measures may reduce the need for water systems upgrades, delaying major investments, but this may also adversely impact these systems, causing water quality issues in distribution networks, degrading the hydraulic performances of sewers, and altering wastewater treatment efficiencies. The widespread deployment of water conservation measures requires technical, financial, social, and political considerations. Further research is still required to better understand its implications for water systems, assess its impact on water and energy use, and support decision-makers in developing water conservation programmes.

As more solar photovoltaic panels are expected to be introduced globally to promote the stabilization and decarbonization of electricity supply, concerns have been raised about the associated resource constraints and the environmental impacts. To promote circular economy initiatives, this study aims to provide insight into the variables and the conditions that play a key role in the expression of environmental impacts and find the better path for a circular economy of solar panels, which requires analyzing impacts on multiple indicators as a result of various factors and their cross-interactions. To achieve this goal, we constructed a resource circulation simulator that considers factors related to the business environment as well as physical factors. The simulation model was then applied to the largest administrative region in Japan, and the parameter space was explored by combining Regional Sensitivity Analysis, a global sensitivity analysis method, and Random Forest to consider the interactions among these variables. As a result of 30,000 Monte Carlo simulations performed on 26 input variables, it was found that the key parameters in final disposal and resource consumption potential differ between the two purposes; avoiding catastrophic loads and achieving ambitious reductions. In contrast, for greenhouse gas emissions, it is important to maintain or increase the efficiency of photovoltaic panel power generation for both purposes. Additionally, in the case where the key variables were optimized in terms of final disposal/resource consumption potential, it will effectively reduce the two impacts to 0.7 % and 12.6 %, respectively, with a minimal negative impact on greenhouse gas emissions. In contrast, in the case when optimized in terms of greenhouse gas emissions, it has only a small impact on greenhouse gas emissions and large negative impacts on final disposal and resource consumption potential. Further refinement of the parameter space and thresholds in the analysis is a topic for future research.

The need to balance ecosystems and ensure the well-being of all people underlines the urgency of closing product life cycles. In recent years, the circular economy (CE) has emerged as one of the most relevant factors in achieving the Sustainable Development Goals. This paper presents a systematic literature review (SLR) of waste management efficiency at the European level. Furthermore, it presents a standard data envelopment analysis (DEA) of 27 European countries over the period 2017–2021, focused on municipal waste. Three models (i.e., economic, technical, sustainable) are proposed to optimise the rates of municipal waste recycling and circular material use.

The SLR, based on an initial set of 216 articles that was subsequently refined through double screening to 31, highlights the strategic role of the waste management, recycling and municipal solid waste triangle. The results of the DEA indicate stronger synergy between technical and sustainability dimensions than between economic and sustainability components. Moreover, they highlight fragmented performance in Europe, with distinct clusters of countries emerging as top performers in each of the three models, and the Netherlands, Slovenia, France, Italy, Germany and Sweden demonstrating superior performance for both CE outcomes and sustainable performance. Overall, the results emphasise the strategic role played by technology in facilitating an efficient circular model of municipal waste management to minimise landfilling and other environmentally detrimental practices, thereby stimulating the development of sustainable communities for optimised waste management, in line with broader sustainability objectives.

Carbon inequality is strongly related to economic development and human well-being (HWB) improvements. However, relatively little research has been undertaken to predict future interregional carbon inequality in China from an HWB equity perspective based on scenarios combining shared socioeconomic pathways (SSPs) and carbon reduction policies. The biproportional scaling method named after economist Richard Stone (RAS) was used to predict China's 2020 to 2050 multiregional input–output (MRIO) tables. Then, these MRIO tables were used to simulate future net carbon emissions (NCEs) and net human well-being (NWB) transfers under the SSP1–1.5 °C, SSP1-NEU, SSP2-2 °C and SSP5-BAU scenarios. China's interregional carbon inequality was predicted under different scenarios to clarify the ideal path for mitigating carbon inequality. In the SSP1–1.5 °C, SSP1-NEU, SSP2-2 °C and SSP5-BAU scenarios, the total carbon emissions (CEs) clearly decrease, whereas the total HWB clearly increases. Transfers of NCEs between regions increases, and transfers of NWB between regions decreases in each of the four scenarios. According to the mean regional environmental inequality (REI) value, China's interregional carbon inequality is relatively low under the SSP1–1.5 °C and SSP1-NEU scenarios and relatively high under the SSP2–2 °C and SSP5-BAU scenarios. Mitigating interregional carbon inequality is a long-term and arduous task that requires commitments from governments, businesses and society. These findings clarify the optimal path for China to reduce carbon inequality in the future and provide a theoretical basis for government agencies to rationally adjust the current development model. Furthermore, they provide a supportive reference to help other economies achieve equitable and sustainable development.

Climate policies will strongly affect future supply chains in ways that can be predicted using integrated assessment models (IAMs). The outcomes of IAMs are now being used to conduct prospective life cycle assessments (pLCA) where the background data reflects expected future changes in the economy. However, the technological representation of emerging technologies is often limited in IAMs, which cover a reduced number of routes, thus offering limited insights into their role in future scenarios. This study addresses this gap by integrating emerging technologies omitted in IAMs into future markets, providing a more robust foundation for pLCAs. Diesel, widely used in transportation, heating, and power systems, has established itself as an integral part of the world's infrastructure. Hence, to illustrate our approach, here we analyze the future environmental impacts of heavy-duty trucks fueled with synthetic Fischer-Tropsch e-diesel, incorporating our technology in the diesel market of the background system, through an integrated LCA approach. The standard non-integrated LCA would analyze these technologies in the foreground, assuming that the background is given. In contrast, our integrated LCA, which is particularly suited for cases where technologies in the foreground are deployed at scale, makes both systems consistent with each other. Our findings reveal mismatches in climate impacts depending on the climate pathway and technology of up to 35 % between the integrated and non-integrated approaches, which increase over time, particularly from 2020 to 2050, and are more pronounced when assessing highly carbon-negative or carbon-positive technologies. Overall, we stress the importance of having consistent foreground and background systems for performing more meaningful and accurate LCAs. Moreover, we provide detailed guidelines on implementing such integrated analysis in current software packages, aiming to enhance the reliability of pLCAs for emerging technologies.

Cities worldwide are actively implementing waste classification policies to develop efficient waste management systems that address the growing challenges of waste disposal and promote sustainable development. However, selecting tailored waste classification solutions is challenging due to the intricate interrelationships within the system and the complex trade-offs between different management objectives. This study presents a comprehensive methodology that simulates the entire waste classification management system by integrating the stages of waste generation, source classification, and final disposal, all linked by waste quantities and properties. A multi-objective optimization model is employed to identify optimal technical pathways that balance economic, environmental, and energy objectives. This methodology was applied in Zhangjiagang, a medium-sized city in China, revealing that economically optimal solutions focusing on landfill and composting as disposal pathways could exacerbate environmental impacts, with indicators deteriorating by 23.1 %–419.1 % except for freshwater eutrophication potential. In contrast, a multi-objective optimization solution prioritizing incineration and anaerobic digestion achieves environmental and energy benefits valued at 18.7 USD/t, with a modest increase in economic costs of 5.7 USD/t compared to the base year. The unconstrained optimal scenarios achieve net negative environmental costs and recover 2.0 × 10⁶ MJ of energy. The study recommends moderate and dynamically adjusted kitchen waste separation rates depending on the stage of implementation. Additionally, the findings reveal that the “lock-in” effects of mismatched disposal facilities can significantly escalate costs. In conclusion, this study underscores the importance of systematic design and multi-objective optimization, offering a comprehensive methodology for cities aiming to enhance their waste classification strategies.

As China advances toward its carbon peaking goals, many regions face the challenge of balancing rapid economic growth with sustainable development. Evaluating carbon emissions at the provincial level is crucial for formulating effective strategies to achieve China's carbon peak targets. This study aims to construct an accurate model for predicting carbon emissions and to explore the evolution of these emissions across Chinese provinces, as well as their contributions to national carbon peak targets. Using the Environmental Kuznets Curve (EKC) theory, the 30 provinces were categorized into groups. An ensemble carbon emissions forecasting model was developed by integrating time-series models with multifactor models. Three scenarios were established within the framework of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). Monte Carlo simulations were employed to explore potential pathways to achieve carbon peaks. The results indicate that China will reach its carbon emission peak between 2030 and 2031, with peak values expected to range between 11,499.65 and 11,629.51 Mt. Significant differences were observed among the provincial groups in their contributions to carbon peaking. Groups II and III are projected to peak in 2030 and 2022, respectively, while Groups I and IV face greater challenges, with peak years projected between 2032–2035 and 2031–2034, respectively. Four tiers with different emission reduction responsibilities were identified by comparing the peak times of the 30 provinces under the three scenarios, and optimal recommendations for achieving carbon peaks were proposed for each province. The accurate prediction models and Monte Carlo simulations provide reliable results for achieving carbon peak targets across Chinese provinces, offering a scientific basis for optimizing national carbon emission reduction policies.

Nitrous oxide is a significant greenhouse gas (GHG) contributor in crop production, yet detailed documentation, including crop yields and specific emission factors (EF_d of N₂O), are lacking for the North China Plain. A six-year (2016–2022) diversified crop rotation experiment in this region examined GHG emissions from eight crops (sweet potato, peanut, soybean, spring maize, sweet sorghum, ryegrass, summer maize, and winter wheat) and fallow seasons. The results revealed that summer maize had the highest cumulative soil N₂O emissions (5.07 kg N ha⁻¹), with an average flux value of 225.3 μg N₂O-N m⁻² h⁻¹ during the growing season. Summer maize emitted 16–75 % more N₂O than spring crops and winter wheat and 85–86 % more than ryegrass (cover crop) and the winter fallow season. All eight crops acted as weak net CH₄ sinks. Annual average CO₂-equivalents of N₂O and CH₄ emissions reflected N₂O emissions from all crops and fallow seasons. In addition, GHG footprint metrics (GF_a, per unit area; GF_y, per kg equivalent yield; GF_b, per kg biomass; GF_e, per unit economic benefit) based on life cycle assessment thinking showed that intensive cereal crops (winter wheat and summer maize) had the highest GHG footprints, while sweet potato had the lowest due to its greater biomass and lower N input. Other crops had 19–90 % lower GHG footprints than wheat and maize. Furthermore, we also quantified specific-crop EF_d of N₂O, with ryegrass having the lowest EF_d (0.48 ± 0.02 %), followed by winter wheat (0.81 ± 0.43 %), and other crops ranging from 1.00 ± 0.13 % to 2.36 ± 0.85 %. This study provides important emissions data for different crops and winter fallow periods, enhancing our understanding of GHG footprints and emission factors, which are essential for the advancement of sustainable agriculture practices.