Effects of wind-driven current and thermal dynamics in a temperate monomictic reservoir: Implications for manganese transport and treatment in water supply systems

Increasing manganese (Mn) concentrations in source water contribute to aesthetic and health-related concerns in drinking water. The challenges with Mn in drinking water primarily arise from elevated Mn concentrations in the water supply reservoir, with the inefficacy of Mn treatment largely attributed to fluctuating Mn levels in the water source. A three-dimensional Mn cycle model in a temperate monomictic reservoir, Tarago Reservoir, and a decision support system reflecting Mn variations in the local water treatment plant have been established in previous research. This study aimed to examine Mn variations from the reservoir to raw water and treated water under the influence of wind conditions during different stages of thermal structure, and discover valuable recommendations for Mn treatment in the local water supply system. We crafted 12 scenarios to scrutinize the impact of varying intensities of offshore and onshore winds on hydrodynamic processes and Mn transport during strong thermal stratification, weak thermal stratification, and turnover. The scenario analysis revealed that, during the gradual weakening of thermal stratification, offshore wind induced a substantial amount of Mn to the upper layers near the water intake point. Conversely, onshore wind hindered the upward transport of Mn. The simulated Mn in the raw water under the 12 scenarios indicated that the timing of turnover in the Tarago Reservoir is the primary concern for Mn treatment in the water treatment plant. Additionally, close attention should be given to the frequency and intensity of offshore winds during the weakening of thermal stratification.

Change of spermatophyte family diversity in distribution patterns with climate change in China

The global climate is undergoing extraordinary changes, profoundly influencing a variety of ecological processes. Understanding the distribution patterns and predicting the future of plant diversity is crucial for biodiversity conservation in the context of climate change. However, current studies on predictive geographic patterns of plant diversity often fail to separate the effects of global climate change from other influencing factors. In this study, we developed a spatial simulation model of spermatophyte family diversity (SSMSFD) based on data collected from 200 nature reserves covering approximately 1,500,000 km2, where direct anthropogenic disturbances to plant diversity and the surrounding environment are absent. We predicted the spermatophyte family diversity for all provinces in China in 2020, 2040, and 2080, considering the impacts of global climate change. On average, China currently exhibits 118 plant families per 25 km2, with a decreasing trend from southeast to northwest. When considering only the effects of global climate change, excluding direct anthropogenic disturbances, our results indicate that under the Shared Socioeconomic Path Scenarios (SSPs) 245 and 585, spermatophyte family diversity is projected to slowly increase in most Chinese provinces from 2021 to 2080. Notably, the increase is more pronounced under SSPs585 compared to SSPs245. Global climate change has a positive effect on plant diversity, in contrast to the negative impact of anthropogenic disturbances that often lead to declines in plant diversity. This research highlights the contrasting outcomes of future plant diversity under the sole influence of global climate change and the significant negative effects of anthropogenic disturbances on diversity.

How to effectively achieve air pollutant reduction and carbon mitigation in China's industrial sector? A study based on decomposition analysis and scenario simulation

Reducing air pollutant and carbon emissions in the industrial sector is crucial for the ecological civilization construction in China. In this study, we first employ the generalized Divisia index method to analyze the driving factors of industrial CO2 and SO2 emissions, incorporating fixed asset investment and R&D input. The key sub-sectors that exert significant impact on emissions of the whole industrial sector are identified. And then, scenario analysis and Monte Carlo simulation are utilized to predict future trends and potential for reducing CO2 and SO2 emissions. Furthermore, the carbon peaking time of the industrial sub-sectors is investigated. The results indicate that fixed asset investment and R&D input both have played positive roles in CO2 and SO2 emissions. Emission reduction is mainly driven by investment emission intensity, output emission intensity, and R&D emission intensity. Sub-sectors S09, S10, S11, S12, and S18 present substantial potential for reducing air pollutant and carbon emissions. Although SO2 emissions from the industrial sector are projected to decrease in the future, the peak of CO2 emissions have not been reached. The carbon peak time for the whole industrial sector is predicted in 2025, with the peak of 7892.33 Mt. The five key sub-sectors are anticipated to reach the respective carbon emission peaks at different times. Therefore, to effectively implement industrial air pollutant and carbon reduction, the role of fixed asset investment and R&D input should be fully utilized, and high-energy consumption and high-emission sub-sectors should be prioritized for action.

Scenario analysis of the eco-efficiency for municipal solid waste management: A case study of 211 cities in western China

Mitigating greenhouse gas (GHG) emissions is vital for creating sustainable municipal solid waste management systems (MSWMS). In this study, we constructed an MSWMS considering recycling and carried out GHG emission accounting for MSWMS in western China from 2012 to 2021 based on the IPCC mass balance (MB) method. Then, we modeled the emission reduction potentials and economic benefits under different scenarios for 211 prefectural and county-level cities. We formed an eco-efficiency analysis framework that can be used to explore the sustainable development mode. Results revealed that: (1) Emissions from the western region's municipal solid waste (MSW) disposal exhibit an inverted "U" pattern, increasing at an annual rate of about 1.3 % since 2012, peaking in 2019, and then decreasing at rates of 14.4 % and 10.6 %. (2) The GHG emissions show a spatial pattern of decreasing evolution from east to west and south to north, and the provincial-municipal level shows different trends. (3) The SB3 scenario (optimization of landfill gas power generation technology) was the most ecologically efficient in 43 % of the western cities, followed by SB4 (33 %) and SA3 (24 %). (4) The development of integrated urban domestic waste management strategies by the three-level scenarios derived from this study will help local governments achieve the goal of sustainable urban development. Clarifying the differences in GHG emissions and eco-efficiency among cities will help provide policy recommendations for regions with similar characteristics to explore technically applicable, economically affordable implementation paths for city management according to local conditions.

Scenario analysis of energy consumption and related emissions in the transportation industry-a case study of Shaanxi Province

In the context of pursuing carbon neutrality and balancing the use of fossil fuels with renewable energy, the transportation industry faces the challenge of accurately predicting energy demand, related emissions, and assessing the effectiveness of energy technologies and policies. This is crucial for formulating energy management plans and reducing carbon dioxide (CO2) and atmospheric pollutant emissions. Currently, research on energy consumption and emission forecasting primarily relies on energy consumption quantities and emission factors, which lack precision. This study employs the low emissions analysis platform (LEAP) model, utilizing a "bottom-up" modeling approach combined with scenario analysis to predict and analyze the energy demand and related emissions in the transportation industry. Compared to previous studies, the methodological framework proposed in this research offers higher precision and can explore energy-saving and emission-reduction pathways for different modes of transport, providing a valuable energy forecasting tool for transport policy formulation in other regions. The forecast results indicate that under the business-as-usual (BAU) scenario, by 2049, the energy consumption and related emissions in Shaanxi Province's transportation industry are expected to increase by 1.15 to 1.85 times compared to the baseline year. In the comprehensive (CP) scenario, the industry is projected to reach a carbon peak around 2033. The study also finds that energy consumption and emissions predominantly originate from private passenger vehicles, highway freight, and civil aviation passenger, which have the greatest potential for emission reduction under the transport structure optimized (TSO) scenario. Therefore, policymakers should consider regional development characteristics, combine different transportation modes, and specifically analyze the emission reduction potential of the transportation industry in various regions, formulating corresponding reduction policies accordingly.

Simulation of vertical migration behaviors of heavy metals in polluted soils from arid regions in northern China under extreme weather

Heavy metal migration behaviors and mechanisms in soils are important for pollution control and remediation. However, there are few related studies in arid areas under extreme weather patterns. In this study, we developed a one-dimensional continuous point source unsaturated solute transport model, and utilized Hydrus-1D to simulate the transport of Cu, As and Zn, in the pack gas zones of soils within the impact areas of two typical mining areas in Inner Mongolia. The results show that the soil has a significant interception capacity, with a short heavy metal vertical migration distance of ≤100 cm. Soil texture and heavy metal sorption affinity are two key factors that influence heavy metal transport. In soils with high contents of sands but low contents of clays, heavy metals have large mobility and thus migrate deeper and are more evenly distributed in the soil profile. The migration of different heavy metals in the same soil also varies considerably, with large migration depth for metals having low binding affinities onto soils. Scenario analysis for extreme drought and rainfall shows that, rainfall amount and intensity are positively correlated with heavy metal transport depth and negatively correlated with the peak concentration. Increasing rainfall/intensity results in a more uniform distribution of heavy metals, and lower profile concentrations owing to enhanced horizontal dispersion of surface runoff. When the total amount and intensity of rainfall remain constant, continuous or intermittent rainfall only affects the transport process but has almost no effect on the final pollutant concentration redistribution in the soil. These results provide theoretical data for estimating the degree of heavy metal pollution, and help design control and remediation strategies for polluted soils.

History and future of water footprint in the Yangtze River Delta of China

Quantifying the drivers of water footprint evolution in the Yangtze River Delta is vital for the optimization of China's total water consumption. The article aims to decompose and predict the water footprint of the Yangtze River Delta and provide policy recommendations for optimizing water use in the Yangtze River Delta. The paper applies the LMDI method to decompose the water footprint of the Yangtze River Delta and its provinces into five major drivers: water footprint structure, water use intensity, R&D scale, R&D efficiency, and population size. Furthermore, this paper combines scenario analysis and Monte Carlo simulation methods to predict the potential evolution trends of water footprint under the basic, general, and enhanced water conservation scenario, respectively. The results show that (1) the expansion of R&D scale is the main factor promoting the growth of water footprint, the improvement of R&D efficiency, and the reduction of water intensity are the main factors inhibiting the increase of water footprint, and the water footprint structure and population size have less influence on water footprint. (2) The evolution trend of water footprint of each province under three scenarios is different. Compared to the basic scenario, the water footprint decreases more in Shanghai, Zhejiang, and Anhui under the general and enhanced water conservation scenario. The increase in water footprint in Jiangsu under the enhanced scenario is smaller than that of the general water conservation scenario.

Environmental impact of Norwegian self-selected diets: comparing current intake with national dietary guidelines and EAT-Lancet targets

OBJECTIVES

Dietary environmental impact in a Norwegian adult population was estimated for six environmental impact categories. Moreover, environmental benefits of scenario diets complying with the Norwegian Food-Based Dietary Guidelines (FBDG) and the EAT-Lancet reference diet were assessed.

DESIGN

The current diet of Norwegian adults was estimated according to 24-h dietary recall data from a national dietary surveillance survey (Norkost 3). Scenario diets were modelled to represent the Norwegian FBDG and the EAT-Lancet healthy reference diet. Dietary environmental impact in terms of global warming potential, freshwater and marine eutrophication, terrestrial acidification, water use and transformation and use of land was estimated for the current and scenario diets using environmental impact data representative of the Norwegian market. Significant associations between impact and gender/educational attainment were assessed at P < 0·05.

SETTING

Norway.

PARTICIPANTS

Adults (n=1787) aged 18-70 years who participated in the Norkost 3 survey (2010-2011).

RESULTS

Environmental impact varied significantly by gender and educational attainment. The food groups contributing most to environmental impact of Norwegian diets were meat, dairy, beverages, grains and composite dishes. Compared with the current Norwegian diet, the FBDG scenario reduced impacts from 2 % (freshwater eutrophication) to 32 % (water use), while the EAT-Lancet scenario reduced impacts from 7 % (marine eutrophication) to 61 % (land use). The EAT-Lancet scenario resulted in 3-48 % larger reductions in impact than the FBDG scenario.

CONCLUSIONS

The Norwegian FBDG, while not as environmentally friendly as the EAT-Lancet reference diet, can still be an important tool in lessening environmental burden of Norwegian diets.

A study on the dynamic impact of carbon emission trading on green and high-quality development

As a pivotal element in market mechanisms, carbon trading is increasingly recognized as crucial for achieving China's Carbon Peaking and Carbon Neutrality Goals. This study introduces a comprehensive dynamic model, integrating carbon trading, emissions, economic growth, and green technology innovation, to offer a holistic understanding of the interplay between these domains. Utilizing principles from nonlinear dynamics and chaos theory, the model is adept at simulating various scenarios and assessing the effectiveness of government policies in stabilizing these complex systems. In-depth analysis provided by this research sheds light on the nuanced impact of carbon trading policies on sustainable development. Key findings highlight (1) Carbon trading's essential role as a catalyst in propelling sustainable and high-quality growth. (2) A strong positive relationship is observed between the sophistication of the carbon trading mechanism and its effectiveness in stimulating green technology innovation and fostering high-quality green development. Notably, carbon trading's influence on green technology innovation markedly enhances the efficacy of carbon emission reduction strategies. (3) Government regulations are instrumental in augmenting carbon prices, thus incentivizing increased corporate participation in emission reduction and enhancing the overall impact of carbon emission reduction. Nevertheless, the study identifies a critical threshold in regulatory intensity, beyond which there is a risk of system destabilization (a 3 ≥ 0.032 ). These findings underscore the imperative for developing an integrated national carbon emission trading market, prioritizing sustainable growth strategies and diligently pursuing China's environmental objectives.

Electrification of public buses in Jakarta, Indonesia: A life cycle study

Indonesia plans to mitigate the environmental emissions, particularly the carbon emissions, from the transport by replacing conventional buses with battery electric buses (BEBs). However, there are limited studies on the potential environmental benefits of BEBs and mostly focused on carbon emissions. In this study, the environmental impacts of adopting BEBs in Jakarta's public transportation system were examined using Life Cycle Assessment (LCA) to better understand its potential environmental impacts. Using LCA, the environmental impacts of BEBs were also compared with conventional buses across their life cycles, which included raw materials extraction until the end of life stages. The results showed diesel buses have generally lower environmental impacts than BEBs due to the high share of fossil fuels in the electricity generation in Indonesia. Scenario analysis showed that extending the life cycle, using different battery disposal methods, and using battery reuse could lead to higher environmental benefits in using BEBs. Among the scenarios considered in the study, prolonging the lifespan of the bus to 32 years, using electricity mix with a higher share of renewable energy and reusing the lithium-ion batteries, BEBs would have lesser environmental impact per kilometre. In particular, the particulate matter formation (PM2.5) dropped 21 %, while the overall life cycle of BEB using the highest renewable scenario showed an average of 25 % improvement compared to the baseline scenario regarding environmental impact.

Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

Scenario analysis of the socioeconomic impacts of achieving zero-carbon energy by 2030

This study uses scenario analysis to assess the socioeconomic impacts of achieving zero-carbon energy by 2030. Three scenarios are developed: 1) business as usual; 2) accelerated deployment of renewable energy and electric vehicles; and 3) scenario 2 plus comprehensive energy efficiency improvements. Quantitative models are used to evaluate the impacts on employment, productivity, consumer costs, inequality and energy security under each scenario. The results show that scenario 3, with the most ambitious decarbonization and efficiency measures, can generate the most jobs (2.1 million more than business as usual) and the lowest consumer costs (12% reduction). However, it may also lead to a small productivity loss (1.2% lower than business as usual) due to higher costs of new technologies. Income and health inequality are projected to decrease across all scenarios due to improved energy access and reduced fuel poverty. Energy security is expected to improve significantly in scenarios 2 and 3 due to reduced oil dependence. This study provides an analytical framework to assess the integrated socioeconomic impacts of zero-carbon transitions under uncertainty. The scenarios and findings can inform policymaking by highlighting the opportunities and challenges around the low-carbon transition, enabling decision makers to maximize benefits and minimize negative consequences.

Uncertain SEIAR system dynamics modeling for improved community health management of respiratory virus diseases: A COVID-19 case study

The study investigates the significance of employing advanced systemic models in community health management, with a focus on COVID-19 as a respiratory virus. Through the development of a system dynamics model integrating an uncertain SEIAR model, our research addresses the critical issue of parameter uncertainty using Ensemble Kalman Filter (EnKF) and Metropolis-Hastings (MH) algorithms. We present a case study using real COVID-19 outbreaks in Iran, offering insights into effective outbreak control scenarios and considering the global impact of respiratory viruses. The research yields distinctive results, showcasing variable mortality rates (40,500 to 436,500) across scenarios in Iran. Model accuracy is rigorously evaluated using the Normalized Root-Mean-Square Deviation (NRMSD) for new cases, deaths, and recoveries (0.2 %, 1.2 %, and 0.6 % respectively). The outcomes not only contribute to the existing body of knowledge but also offer practical implications for healthcare policies, economic considerations, and sensitivity assessments related to respiratory diseases. This study stands out from others in its approach to modeling and addressing uncertainty within a system dynamics framework. The integration of EnKF and MH algorithms provides a nuanced understanding of parameter uncertainty, adding a layer of sophistication to the analysis. The application of the model to real-world COVID-19 outbreaks in Iran further enhances the study's relevance and applicability. In conclusion, the research introduces an uncertain SEIAR system dynamics model with unique contributions to policymaking, economic considerations, and sensitivity assessments for respiratory diseases. The outcomes and insights derived from the study not only advance our understanding of disease dynamics but also provide actionable information for effective public health management.

The global emission mitigation potential of avoiding waste and product lifespan extension by Chinese households

This paper examines the emission mitigation potential of Chinese households' low-carbon behavior by 2030 through a global carbon footprint scenario analysis. The emission reduction effect is estimated by comparing the projected global emissions in 2030 in a lifestyle emulation scenario and a low-carbon scenario, in which Chinese households adopt low-carbon consumption behaviors. Lifestyle emulation is modeled based on what we call "world Engel curves", which describe how the expenditure share of a certain consumption good depends on the total per capita expenditures for household consumption (which depends on income). By including a dynamic link between household lifestyle changes and GDP, we then obtain the emission projections under different scenarios in 2030, based on the historical data for 49 countries from 1995 to 2011 from EXIOBASE. Our results show that adopting a mild low-carbon lifestyle by households helps only little in terms of reducing GHG emissions. Reducing avoidable waste and expanding the lifetime of products are not enough to help meeting the 2 °C goal. More drastic changes are required.

Impact of forest landscape restoration in combating soil erosion in the Lake Abaya catchment, Southern Ethiopia

As an effect of forest degradation, soil erosion is among Ethiopia's most pressing environmental challenges and a major threat to food security where it could potentially compromise the ecosystem functions and services. As the effects of soil erosion intensify, the landscape's capacity to support ecosystem functions and services is compromised. Exploring the ecological implications of soil erosion is crucial. This study investigated the soil loss and land degradation in the Lake Abaya catchment to explore forest landscape restoration (FLR) implementation as a possible countermeasure to the effects. The study used a geographic information system (GIS)-based approach of the Revised Universal Soil Loss Equation (RUSLE) to determine the potential annual soil loss and develop an erosion risk map. Results show that 13% of the catchment, which accounts for approximately 110,000 ha, is under high erosion risk of exceeding the average annual tolerable soil loss of 10 t/ha/year. Allocation of land on steep slopes to crop production is the major reason for the calculated high erosion risk in the catchment. A scenario-based analysis was implemented following the slope-based land-use allocation proposal indicated in the Rural Land Use Proclamation 456/2005 of Ethiopia. The scenario analysis resulted in a reversal erosion effect whereby an estimated 3000 t/ha/year of soil loss in the catchment. Thus, FLR activities hold great potential for minimizing soil loss and contributing to supporting functioning and providing ecosystem services. Tree-based agroforestry systems are among the key FLR measures championed in highly degraded landscapes in Ethiopia. This study helps policymakers and FLR implementors identify erosion risk areas for future FLR activities. Thereby, it contributes to achieving the country's restoration commitment.

Net-zero energy transition in ASEAN countries: The evolutionary model brings novel perspectives to the cooperative mechanism of climate governance

In an era marked by escalating climate change, the fragile ecological balance faces increasing strain. Whilst significant knowledge exists regarding the accumulation of carbon emission within the Association of Southeast Asian Nations, little is known about when and how countries could reach net-zero emission goal as agreed in Paris Agreement. For this purpose, our study examines the primary driving factors of carbon emission from 1990 to 2020 across the ten countries using the Logarithmic Mean Divisia Index model. We leverage the random forest model to explore the net-zero scenarios and the Autoregressive Integrated Moving Average approach to identify the evolutionary trajectories of carbon emission trends. Our findings underscore the imperative need for expediting decarbonization efforts, emphasizing the urgency for widespread adoption of clean technologies and substantial investment in green initiatives. Countries at similar stages of progress might establish a cooperation mechanism of clean energy base construction, energy storage allocation and policy formulation. These insights can help us better estimate future demand of clean energy, explore strategies for decarbonization, and inform historical commonalities of carbon emission growth.

Study on carbon emission reduction countermeasures based on carbon emission influencing factors and trends

The carbon mitigation response encompasses a variety of strategies aimed at mitigating greenhouse gas emissions resulting from human activities. These measures are crafted to address the challenges posed by climate change and facilitate the transition of businesses towards a low-carbon paradigm. Leveraging the analytical outcomes of the extended STIRPAT model and the PSO-BP prediction model, this paper suggests countermeasures for reducing carbon emissions in China's metal smelting industry. The overarching objective is to contribute to China's attainment of the "dual carbon objectives." The study identifies key factors influencing carbon emissions in the metal smelting industry, ranked in descending order of sensitivity: population, coal consumption, urbanization rate, total metal production, carbon intensity, proportion of secondary industry, and GDP per capita. Results from three established scenarios-namely, low carbon, standard, and high carbon-indicate a consistent decline in carbon emissions from China's metal smelting industry over the next 15 years. This research not only enhances the findings of existing studies on carbon emissions in the metal smelting sector but also introduces an innovative approach to carbon emission reduction within China's metal smelting industry.

Predicting low carbon pathways on the township level in China: a case study of an island

Carbon prediction on the township level is usually difficult due to a lack of necessary information. To fulfil the research gap, the study focused on a town located in a nearshore island (Lingshan) in China. A questionnaire survey was performed to collect essential information about the future development of the town, followed by validating interviews with the island management committee. The carbon prediction of the town was established by the Low Emissions Analysis Platform (LEAP) model. The baseline scenario reflecting the existing method of carbon management was compared with an alternative low-carbon scenario. The prediction from 2020 to 2060 covers the periods of the planned carbon emissions peak in 2030 and carbon neutrality in 2060. It is found that energy-related activities and electricity consumption are the primary contributors to carbon emissions on the island. The carbon emission of Lingshan Island increases from -1333 tCO2e in 2020 to 2744 tCO2e in 2060, and the carbon peak target cannot be achieved in the baseline scenario. However, the carbon emission of the low-carbon scenario is predicted to have a peak of -850 tCO2e in 2029. The prediction model developed in this study, along with the proposed policy recommendations, can be applied to other towns or regions where data availability is limited to promote carbon reduction.

Assessing the phosphorus cycle in European agricultural soils: Looking beyond current national phosphorus budgets

Phosphorus (P) is an essential nutrient for all crops, yet its excess negatively affects public health, the environment, and the economy. At the same time, rock P is a critical raw material due to its importance for food production, the finite geological deposits, and its unequal regional distribution. As a consequence, nutrient management is addressed by numerous environmental policies. Process-based biogeochemical models are valuable instruments to monitor the P cycle and predict the effect of agricultural management policies. In this study, we upscale the calibrated DayCent model at European level using data-derived soil properties, advanced input data sets, and representative management practices. Our results depicted a P budget with an average P surplus (0.11 kg P ha-1 year-1), a total soil P (2240.0 kg P ha-1), and available P content (77.4 kg P ha-1) consistent with literature and national statistics. Through agricultural management scenarios, we revealed a range of potential changes in the P budget by 2030 and 2050, influenced by the interlink of P with biogeochemical carbon and nitrogen cycles. Thus, we developed a powerful assessment tool capable of i) identifying areas with P surplus or deficit at high spatial resolution of 1 km2, (ii) pinpointing areas where a change in agricultural management would be most urgent to reach policy goals in terms of environmental pollution, food security and resource efficiency of a critical raw material, and iii) assessing the response of the P cycle to modifications in agricultural management.

Adapted forest management to improve the potential for reindeer husbandry in Northern Sweden

In northern Sweden, improvements of grazing conditions are necessary for the continuation of traditional, natural pasture-based reindeer husbandry. Ground and tree lichen constitute the main fodder resource for reindeer during winter but have reached critically low levels. Using a forest decision support system, we prescribe adapted forest management to improve the preconditions for reindeer husbandry and compare outcomes with the continuation of current forest management. We found that adapted management increases the forest area with ground lichen habitat by 22% already within 15 years, while a continuation of current management would result in a further decrease in ground lichen. Tree lichen habitat can be retained and increased in all scenarios, which is important in a changing climate. Compared to a continuation of current practices, adapted management with significantly improved conditions for lichen resulted in a decrease in net revenues from wood production by 11-22%.

"Land-sparing benefits biodiversity while land-sharing benefits ecosystem services": Stakeholders' perspectives on biodiversity conservation strategies in boreal forests

Biodiversity conservation and economic profit from forests can be combined by various land-sparing and land-sharing approaches. Using a semi-structured survey, we evaluated support for scenarios representing contrasting conservation strategies in a managed boreal forest landscape. Land-sparing approaches were supported by the conservation organisation, regional administrations and the forest company, mainly motivated by the benefit for biodiversity based on ecological theory. Land-sharing approaches were supported by one recreational organisation, some municipalities and the forest owners' association, mainly motivated by the delivery of ecosystem services. Stakeholder groups using certain ecosystem services had motivations that we related to an anthropocentric mindset, while others focused more on species conservation, which can be related both to an anthropocentric or an ecocentric mindsets. Forest conservation planning should consider stakeholders' preferences to handle land-use conflicts. Since reaching consensus among multiple stakeholders seems unfeasible, a combination of land-sparing and land-sharing approaches is probably the best compromise.

Overlooked CO2 emissions induced by air pollution control devices in coal-fired power plants

China's efforts to mitigate air pollution from its large-scale coal-fired power plants (CFPPs) have involved the widespread use of air pollution control devices (APCDs). However, the operation of these devices relies on substantial electricity generated by CFPPs, resulting in indirect CO₂ emissions. The extent of CO₂ emissions caused by APCDs in China remains uncertain. Here, using a plant-level dataset, we quantified the CO₂ emissions associated with electricity consumption by APCDs in China's CFPPs. Our findings reveal a significant rise in CO₂ emissions attributed to APCDs, increasing from 1.48 Mt in 2000 to 51.7 Mt in 2020. Moreover, the contribution of APCDs to total CO₂ emissions from coal-fired power generation escalated from 0.12% to 1.19%. Among the APCDs, desulfurization devices accounted for approximately 80% of the CO₂ emissions, followed by dust removal and denitration devices. Scenario analysis indicates that the lifespan of CFPPs will profoundly impact future emissions, with Nei Mongol, Shanxi, and Shandong provinces projected to exhibit the highest emissions. Our study emphasizes the urgent need for a comprehensive assessment of environmental policies and provides valuable insights for the integrated management of air pollutants and carbon emissions in CFPPs.

Black carbon emissions inventory and scenario analysis for Pakistan

Black carbon (BC) emissions, resulting from the incomplete combustion of carbonaceous fuels, have been extensively linked to adverse impacts on air quality, climate change, and public health. Nevertheless, there is currently a lack of a comprehensive analysis that integrates activity-based BC emissions inventory and scenario analysis at the national/regional, sectoral, and sub-sectoral levels in Pakistan. This study aims to fill this gap by conducting a comprehensive evaluation of Pakistan's BC emissions inventory for 2021 along projecting emissions until 2050 under the reference emission scenario (RES) and the accelerated reduction scenario (ARS) using the GAINS modeling framework to assess the potential impact of mitigation measures. This study takes a unique approach by considering commonly overlooked sources of BC emissions, such as kerosene lighting, brick kilns, diesel generator sets, and natural gas flaring, which are not typically included in conventional analyses. National BC emissions in 2021 were estimated at 181 kt, with residential combustion being the major contributor, accounting for more than half (108 kt) of the total emissions. The transport, industry, waste, agriculture, power plants, and fuel conversion sectors contributed 26.1 kt, 20.1 kt, 10.7 kt, 8.9 kt, 6.0 kt, and 0.9 kt, respectively. We anticipate that the total BC emissions in Pakistan will reach 201 kt under the RES and 41 kt under the ARS scenario by the year 2050. The ARS achieves substantial BC reductions by the adoption of cleaner fuels, improved biomass stoves, end-of-pipe emission control technologies with higher removal efficiencies, and implementing a ban on the open burning of waste and crop residues. This study underscores the considerable potential for reducing BC emissions across various sectors in Pakistan over the next three decades.

Methane emission and influencing factors of China's oil and natural gas sector in 2020-2060: A source level analysis

The Chinese oil and gas industry requires targeted policies to reduce methane emissions. To achieve this goal, it is necessary to predict future methane emission trends and analyze the factors that influence them. However, changing economic development patterns, insufficient analysis of various factors influencing emissions, and inadequate resolution of methane emission inventories have made these goals difficult to achieve. Accordingly, this study aims to expand the methane emission estimation method to compile source-level emission inventories for future emissions, analyze the factors influencing them, and form a mechanistic understanding of the methane emissions from the local oil and gas industry. The research results indicate that methane emissions deriving from this industry will increase rapidly before 2030, after which they will decline slowly in all scenarios. The production and utilization processes in the natural gas supply chain, i.e., compressors and liquid unloading, include the main sources of methane emissions. Emissions are affected significantly by total production and consumption. Change in the overall supply and demand of natural gas affects change in methane emissions more significantly than adopting new technologies and strengthening facility maintenance, i.e., the overall supply and demand of natural gas are the dominant factors in controlling methane emissions. This study suggests that controlling the total demand for oil and gas should be at the core of the methane emission control policy for the local oil and gas industry. Moreover, equipment maintenance and emission reduction technologies should be used more effectively to reduce total emissions.

Unveiling the potential for artificial upwelling in algae derived carbon sink and nutrient mitigation

Mariculture algae may present a crucial part of ocean-based solutions for climate change, with the ability to sequester carbon and remove nutrients. However, the expansion of mariculture algae faces multiple challenges. Here, we measure the changes in algae derived carbon sinks and nitrogen (N) and phosphorus (P) removal between 2010 and 2020 in Shandong Province, China. We further identify the key driving factors, namely area, algal species proportion, and yield, that influence the changes. The results show that algae derived carbon sinks and nutrient removal growth rates in Shandong Province have slowed significantly since 2014, mainly due to area limitations, laver-oriented species change, and unstable yields. Artificial upwelling (AU) has the potential to enhance the yield and subsequently offset the loss of carbon sinks and nutrient removal caused by negative driving factors. Scenario analysis indicates that a complete deployment of AU by 2030 will offset up to a 44.52 % decrease in the mariculture algae area, or a 72.57 % increase in the laver share of the algal species combination compared to 2020. Similar conclusions are reached regarding the role of AU in N and P removal. This study also identifies ancillary challenges such as low energy efficiency and high costs faced by applying AU.

Towards a low-carbon future for China's power supply chain: Critical sectors identification and scenario analysis

The power sector is a significant contributor to global carbon emissions and has received widespread attention from scholars; however, the path to achieving supply chain-wide carbon reductions in China from a provincial perspective remains unclear. This study combined multi-regional input-output and betweenness-based methods to identify the critical upstream sectors that indirectly drive large amounts of carbon emissions through power supply chains. The point source data of coal-fired units were collected to ensure the accuracy of the disaggregated input-output table. In addition, a scenario analysis was conducted to examine the effects of different electricity policy combinations on supply chain-wide emissions during the 14th Five-Year Plan (FYP). Our findings indicate that the embodied carbon intensity of the coal-fired power sector in Northwest China is among the highest in the country, ranging from 36.39 to 82.10 tons/10000 CNY. Therefore, the shift of the power sector to Western China during the 14th FYP will partially offset the positive emission reduction effect of the structural transformation of the power system. To achieve a low-carbon power supply chain, it is necessary to improve the production efficiency of critical transmission sectors and the low-carbon technology levels of major emitting sectors. Our results provide valuable insights for provincial governments to plan low-carbon transformation paths for the power sector.

Decoupling analysis and forecast of economic growth from electricity consumption in the Yangtze River Delta region, China

Decoupling economic growth from electricity consumption is essential for energy conservation and emission reduction. Firstly, this paper applies the LMDI decomposition model to analyze the driving factors of electricity consumption in the Yangtze River Delta region. Secondly, scenario analysis and Monte Carlo technique are combined to research the evolutionary trend of electricity consumption from 2020 to 2035, so as to further analyze the decoupling state. Finally, using nonparametric kernel density estimation, this paper studies the evolution trend of decoupling state from 2005 to 2035. The results show that (1) economic growth is the main factor that promotes the increase of total electricity consumption. Domestic intensity and population scale contribute to the increase in total electricity consumption. The primary factor inhibiting the increase of total electricity consumption is production intensity, while industrial structure and urbanization level contribute to the decrease in total electricity consumption. (2) From 2005 to 2035, the decoupling level has been optimizing on the whole, and the internal gap has also reduced, but there still exists obvious internal gap. (3) Under the three scenarios, the evolution trend of production and domestic electricity consumption is the same. During 2020-2035, the production and domestic electricity consumption both show an increasing trend, with the total electricity consumption under the baseline scenario being the highest, followed by the general and the enhanced electricity-saving scenario. Combined with the empirical results of this paper, some policy recommendations are proposed.

Development of an integrated scenario-based stochastic rolling-planning multistage logistics model considering various risks

In this study, a new integrated scenario-based stochastic rolling-planning multistage logistics model is proposed to reduce overall logistics costs. To achieve this goal, two phases were considered in the model. In the first phase, a multi-criteria group decision-making model was developed to select a trustworthy supplier. In the second stage, the selected suppliers were integrated with other stakeholders to develop a rolling-planning-based logistics model using a variety of risky scenarios. Several risk factors including price variability, demand, and quality risks were considered in the model. By considering these risk factors, a new risk-embedded rolling-planning logistics method was established that regulates inventory, stock-out, and overstock problems by constantly controlling the production volume at the manufacturing site based on actual demands. In this model, the supplier's side material quality, price fluctuation risks, and customer-side demand risks were considered simultaneously. To evaluate the performance of the proposed model, a numerical example was set up, and the obtained results were compared with those of another model where fixed volume production and delivery approach was used instead of the rolling-planning approach. To verify the superiority and robustness of the proposed model, its performance was verified through a sensitivity analysis under different experimental conditions. The findings show that in a risk environment, the proposed model estimates lower logistics costs of 2697648.00 units compared to another model whose costs were 2721843.00 units.

Drivers of ozone-related premature mortality in China: Implications for historical and future scenarios

Long-term exposure to ambient ozone (O3) poses a severe public health threat in China. However, the drivers of premature mortality caused by O3 pollution are still poorly constrained, despite being prerequisites for addressing the threat. Here, we demonstrate the contributions of historical and future changes in peak-season O3, population size, age structure, and baseline mortality to China's O3-related mortality using decomposition analysis. From 2013 to 2021, O3-related mortality decreased dramatically from 78.8 (40.8-124.6) to 68.7 (36.0-107.2) thousand, especially in densely populated areas with high pollution. Variations in peak-season O3, population size, age structure, and baseline mortality led to changes in O3-related mortality of +27.3 (14.8-41.3), +2.6 (1.4-4.1), +22.3 (11.5-35.2), and -40.3 (20.9-63.7) thousand, respectively. The influence of peak-season O3 on O3-related mortality shifted from positive during 2013-2019 (+8.4% per year) to negative during 2019-2021 (-8.8% per year), which highly regulated the interannual trend of mortality. From 2021 to 2035, O3-related mortality is expected to increase by 31% in the current context of peak-season O3 levels, primarily caused by increased aging. Even reducing peak-season O3 to the WHO interim target 1 (IT-1) would only reduce O3-related mortality by 3.9%, while a more rigorous standard (IT-2) would prevent 83.7% of mortality. These findings suggest that improving ambient O3 can lead to significant health benefits, but substantial mitigation strategies are merited given the future trend of population aging.

Investigating the dynamic decoupling relationship between regional social economy and lake water environment: The application of DPSIR-Extended Tapio decoupling model

The water environmental problems associated with rapid socioeconomic growth have drawn widespread attention from the government and the public. Revealing the decoupling mechanism between the social economy and lake water environment has become an important breakthrough point to seek the pathways of sustainable economic development. To investigate the decoupling process of the social economy‒lake water environmental system, this study proposes a comprehensive evaluation model, which integrates the Driving force-Pressure-State-Impact-Response (DPSIR) model, projection pursuit method, and Tapio decoupling model; and then applies it to the case study of Hefei City and Lake Chaohu in China in 2021-2035. Three typical scenarios of current, social economy, and water environment are designed and simulated using the DPSIR model to evaluate the dynamic decoupling relationships under various development patterns. We found that the DPSIR indexes had a fluctuating upward trend from 2009 to 2020, with a synchronous improvement trend of the social economy and lake water environment. Meanwhile, the Tapio decoupling analysis showed that the decoupling relationships between socioeconomic driver forces, response strategies and the status of lake water environment was mostly strongly decoupled and weakly decoupled during 2009-2020. However, there was still an inconsistency between the improvement rate of the lake water environment and the increase rate of the response strategies. During the 2021-2035 simulation period, the DPSIR indexes of all scenarios depicts an overall increasing trend. The decoupling states of S&I-D&P and S&I-R generally tend to be consistent under three regulation scenarios. Among them, the water environment scenario outperforms other scenarios, and the social economy scenario performs worst. Overall, the decoupling of the social economy and lake water environment can attribute to both the transformation of socioeconomic development patterns and the increase of water environmental protection efforts.

Modeling the effect of improved sewage disposal rates on ecological status for aquatic organisms in Japan

Improving sewage disposal rates is an important policy for maintaining the health of aquatic organisms in river environments. In Japan, the rate is not yet 100 %. Two measures are necessary to eliminate the discharge of untreated greywater: (1) increase the number of households connected to sewage lines in areas with sewage systems, and (2) replace single-type household onsite wastewater treatment systems (OWTSs) with combined-type systems. To estimate the effect of improving the disposal rate on river water quality, we developed a hydrology-based organic pollution assessment model with a gridded spatial resolution of 250 m to estimate the biochemical oxygen demand (BOD) in rivers in Gunma Prefecture, Japan. We considered three scenarios based on the sewage disposal rate of 70.5 % in 2015. In Scenario A, the disposal rate is increased to 75.2 % in 2030 by increasing the connection rate to sewage lines. In Scenario B, the rate is increased to 88.2 % in 2030 through additional progress in converting from single-to combined-type OWTSs. In Scenario C, the rate reaches 100 % by 2040. The ecological status of rivers was evaluated using taxon richness of Ephemeroptera, Plecoptera, and Trichoptera estimated from its reported relationship to BOD. The number of sites in Gunma Prefecture polluted by organic waste classified as III (poor) and IV (very poor) was estimated to be 1610 under the present state (2015) and decreased to 1212 (25 % reduction) in Scenario A, 619 (62 % reduction) in Scenario B, and 50 (97 % reduction) in Scenario C, with the improvements mainly in small branch rivers. The effects of improved disposal rates were mainly evident in areas with relative high population densities using single-type OWTSs outside of areas with a sewage system, and measures taken in these areas were shown to be effective.

Agricultural Methane Emissions in China: Inventories, Driving Forces and Mitigation Strategies

Identification of the spatial distribution, driving forces, and future trends of agricultural methane (AGM) emissions is necessary to develop differentiated emission control pathways and achieve carbon neutrality by 2060 in China, which is the largest emitter of AGM. However, such research is currently lacking. Here, we estimated China's AGM emissions from 2010 to 2020 and then decomposed six factors that affect AGM emissions via the LMDI model. The results indicated that the AGM emissions in China in 2020 were 23.39 Tg, with enteric fermentation being the largest source, accounting for 43.9% of the total emissions. A total of 39.3% of the AGM emissions were from western China. The main driver of AGM emission reduction was emission intensity, accounting for 59% and 33.7% of methane emission reduction in the livestock sector and rice cultivation, respectively. Additionally, higher levels of urbanization contributed to AGM emission reductions, accounting for 31.3% and 43.0% of the livestock sector and rice cultivation emission reductions, respectively. Based on the SSP-RCP scenarios, we found that China's AGM emissions in 2060 were reduced by approximately 90% through a combination of technology measures, behavioral changes, and innovation policies. Our study provides a scientific basis for optimizing existing AGM emission reduction policies not only in China but also potentially in other high AGM-emitting countries, such as India and Brazil.

Health and economic benefits of heavy-duty diesel truck emission control policies in Beijing

PM2.5 emissions from heavy-duty diesel trucks (HDDTs) have a significant impact on air quality, human health, and climate change, and seriously threaten the UN Sustainable Development Goals. Globally, a series of emission control measures have been implemented to reduce pollution emissions from HDDTs. Current studies assessing the impact of these measures on air quality and human health have mainly used coarse-grained emission data as input to dispersion model, resulting in the inability to capture the spatiotemporal variability of pollutant concentrations and tending to increase the uncertainty of health impact assessment results. In this study, we quantified the impact of pollution control policies for HDDTs in Beijing on PM2.5 concentrations, human health, and economic losses by integrating policy scenario analysis, pollution dispersion simulation, public health impact and economic benefit assessment models, supported by high spatiotemporal resolution emission data from HDDTs. The results show that PM2.5 concentrations from HDDTs exhibit significant spatial aggregation characteristics, with the intensity of aggregation at night being about twice as high as that during the day. The emission hotspots are mainly concentrated in the sixth, fifth and fourth rings and major highways. Compared to the "business as usual" scenario in 2018, the current policy of updating the fuel standard to China VI and the emission standard to China 6 can reduce PM2.5 concentrations by 96.72%, thereby avoiding 612 premature deaths, which is equivalent to obtaining economic benefits of 1.65 billion CNY. This study further emphasizes the importance of high spatiotemporal resolution emission data during traffic dispersion modeling. The results can help improve the understanding of the effectiveness of emission reduction measures for HDDTs from a health benefit perspective.

Building carbon peak scenario prediction in China using system dynamics model

As the issue of global climate change caused by carbon emissions is of great concern, China has proposed achieving its achieve carbon peak goal by 2030. Building carbon emissions account for approximately 50% of China's total carbon emissions. It is crucial to study the time and values of building carbon peaks. In this paper, based on a system dynamics model, logarithmic mean Divisia index model and Monte Carlo simulation, we predict the building carbon peak in China. The following conclusions are obtained: 1) in the baseline scenario, China's building carbon emissions will peak at 5,427 million tons in 2027. In the high-speed development scenario, China's building carbon emissions will peak at 6,298 million tons in 2032. In the coordinated development scenario, the green development scenario, the low-carbon development scenario, and the low-speed development scenario, the peak occurs in 2030 at 5,972 million tons, 5,991 million tons, 5,657 million tons, and 6,329 million tons, respectively. 2) According to the comprehensive simulation, China's building carbon emissions will reach the peak in 2030, with an 80% probability of reaching 5,729-6,171 million tons.

Temporal and spatial analysis of anthropogenic mercury and CO2 emissions from municipal solid waste incineration in China: Implications for mercury and climate change mitigation

The contribution of municipal solid waste incineration (MSWI) to anthropogenic mercury and CO2 emissions have become increasingly important over the past decade. This study developed an inventory of anthropogenic mercury emissions and CO2 emissions during the period of 2014-2020, of MSWI process in China using a bottom-up inventory at the plant level. Overall, national MSWI anthropogenic mercury emissions increased from 2014 to 2020 by province. It was estimated that total 8321.09 kg of anthropogenic mercury emissions from 548 MSWI plants were scattered in 31 provinces of mainland China in 2020. The average intensity of mercury emission in China was 0.06 g·t-1 in 2020, which was much lower than the pre-2010 level. Furthermore, the increased CO2 emission generated by MSWI from 2014 to 2020 is 1.97 times. Anthropogenic mercury emissions and CO2 emissions were concentrated mainly in developed coastal provinces and cities. The general uncertainty of national mercury emissions and CO2 emissions was estimated to be -123% to 323% and -130% to 335%, respectively. Furthermore, future emissions were predicted from 2030 to 2060 based on different scenarios of the independent and collaborative effects of control proposals, the results indicate that the enhancement of advanced air pollution control technologies and effective management of MSWI represent pivotal factors in realizing future reductions in CO2 and mercury emissions. The findings will supplement those for mercury and CO2 emissions, and be useful for relevant policy-making and to improve urban air quality, as well as human health.

Quantitative law and scenario-based forecasting of different land use expansion, based on reliability analysis in mountainous areas

The continuous high-intensity and disorderly expansion of construction land in mountainous areas threatens city development; consequently, the scientific guidance of its sustainable development has become a research hotspot. This work aimed to develop a new theoretical framework for predicting land expansion. Based on DMSP/OLS-Landsat 7 data correction from 2000 to 2019, to ensure data reliability, this study quantitatively analysed the expansion law of land-use and land-cover (LULC) in Huayuan, a typical mountainous area in China. Based on the land expansion law, the patch-generating land use simulation (PLUS) model was used to predict various types of LULCs in different scenarios. The results showed that (1) the reliability of LULC under multi-source spatio-temporal data correction reached more than 0.97. (2) The expansion law of industrial and mining land, urban living land, and traffic land is sprawl, while rural living land is enclaved and the expansion direction and intensity are obviously different. (3) The scale of land expansion in the natural-oriented scenario was significantly higher than that in the humanism-oriented scenario, with a higher value of 199.33 hm2. This study expands the case study of land use analysis and prediction, and provides scientific guidance for different land expansion planning, which can avoid the mismatch and waste of land resources. Furthermore, it also deepens the exploration of LULC identification reliability method and enriches the theory of different land use prediction in mountainous areas.

The co-benefits of electric mobility in reducing traffic noise and chemical air pollution: Insights from a transit-oriented city

Traffic noise is a growing threat to the urban population. Prolonged exposure to traffic noise has been linked to negative health consequences such as annoyance, sleep disturbances and cardiovascular diseases. While electric vehicles are known to have lower noise profiles, the impacts of electric mobility on traffic noise, especially for electrified heavy-duty vehicles, have not been thoroughly examined. This study aims to examine the impacts of both electric light-duty vehicles and electric buses on traffic noise levels in a highly urbanized city. Traffic noise along the source line and pedestrian network was first estimated and mapped to illustrate its spatiotemporal variations. Then, scenario analysis was used to compare the impacts. Population potentially benefiting from reduced traffic noise in the neighbourhoods and the associated health impacts were also estimated. Results indicate that electric buses have a greater potential to reduce traffic noise, with a maximum reduction of 4.4 dBA during daytime in the urban cores. With all bus fleet electrified, around 60% of the population can benefit from a reduction of 1 dBA at the street environment, 15.3% for 1-2 dBA, and 4.3% for more than 2 dBA. The estimated reduction of preventable deaths and preventable cases of diseases per 100,000 population are 4.15 and 112.99 respectively. The findings shed important insights into prioritizing bus routes to be electrified in urban areas for maximizing health co-benefits.

A supplementary assessment system of AQI-V for comprehensive management and control of air quality in chemical industrial parks

Volatile organic compounds (VOCs) are the dominant pollutants in industrial parks. However, they are not generally considered as part of the air quality index (AQI) system, which leads to a biased assessment of pollution in industrial parks. In this study, a supplementary assessment system of AQI-V was established by analyzing VOCs characteristics with vehicle-mounted PTR-TOFMS instrument, correlation analysis and the standards analysis. Three hourly and daily scenarios were considered, and the hierarchical parameter setting was further optimized by field application. The hourly and daily assessments revealed the evaluation factors for the discriminability of different air quality levels, practiced value for regional air quality improvement, and the reservation of general dominant pollutants. Finally, the universality testing in ZPIP successfully recognized most of the peaks, with 54.76%, 38.39% and 6.85% for O₃, VOCs and NO₂ as the dominant pollutant, and reflected the daily ambient air quality condition, together with the dominant pollutant. The AQI-V system with VOCs sub-index is essential for air quality evaluation in industrial parks, which can further provide scientific support to control the pollution of VOCs and the secondary pollutant, therefore significantly improve the air quality in local industrial parks.

Trajectories of soil microbial recovery in response to restoration strategies in one of the largest and oldest open-pit phosphate mine in Asia

Southwestern China has the largest geological phosphorus-rich mountain in the world, which is seriously degraded by mining activities. Understanding the trajectory of soil microbial recovery and identifying the driving factors behind such restoration, as well as conducting corresponding predictive simulations, can be instrumental in facilitating ecological rehabilitation. Here, high-throughput sequencing and machine learning-based approaches were employed to investigate restoration chronosequences under four restoration strategies (spontaneous re-vegetation with or without topsoil; artificial re-vegetation with or without the addition of topsoil) in one of the largest and oldest open-pit phosphate mines worldwide. Although soil phosphorus (P) is extremely high here (max = 68.3 mg/g), some phosphate solubilizing bacteria and mycorrhiza fungi remain as the predominant functional types. Soil stoichiometry ratios (C:P and N:P) closely relate to the bacterial variation, but soil P content contributes less to microbial dynamics. Meanwhile, as restoration age increases, denitrifying bacteria and mycorrhizal fungi significantly increased. Significantly, based on partial least squares path analysis, it was found that the restoration strategy is the primary factor that drives soil bacterial and fungal composition as well as functional types through both direct and indirect effects. These indirect effects arise from factors such as soil thickness, moisture, nutrient stoichiometry, pH, and plant composition. Moreover, its indirect effects constitute the main driving force towards microbial diversity and functional variation. Using a hierarchical Bayesian model, scenario analysis reveals that the recovery trajectories of soil microbes are contingent upon changes in restoration stage and treatment strategy; inappropriate plant allocation may impede the recovery of the soil microbial community. This study is helpful for understanding the dynamics of the restoration process in degraded phosphorus-rich ecosystems, and subsequently selecting more reasonable recovery strategies.

Effects of varying the spatial configuration and scale of terraces on water and sediment loss based on scenario simulation within the Chinese Loess Plateau

To optimize soil and water conservation measures, it is important to consider the spatial configuration and construction scale of terraces on the Loess Plateau in China. However, there are few existing efficient technology frameworks for assessing the impact of changing the spatial configuration and scale on reducing water and sediment loss at the basin scale. To address this gap, this study proposes a framework that employs a distributed runoff and sediment simulation tool coupled with multi-source data and scenario setting methods to identify the impacts of constructing terraces with different spatial configurations and scales on reducing water and sediment loss at the event scale on the Loess Plateau. Four scenarios (i.e. baseline, realistic, configuration changing and scale changing scenarios) were established to evaluate the associated impacts. The results show that, under the realistic scenario, the average water loss reductions within Yanhe Ansai and Gushanchuan Basins are 15.28 % and 8.68 %, respectively, and average sediment reduction rates are 15.97 % and 7.83 %, respectively. The effect of reducing water and sediment loss in the basin is highly related to the spatial configuration of terraces and that terraces should be built as low as possible on hillslopes. The results also show that, if terraces are disorderly constructed, the threshold of the terrace ratio that effectively contains the sediment yield in the hilly and gully regions of the Loess Plateau is approximately 35 %, whereas if the scale of terraces is increased, the sediment reduction effect is not significantly improved. Furthermore, if terraces are configured near the downslope, the threshold of the terrace ratio that can effectively contain sediment yield is further reduced to approximately 25 %. This study can be used as a scientific and methodological reference for optimizing terrace measures at a basin scale in the Loess Plateau and in other similar regions in the world.

Prediction of CO2 emissions in China by generalized regression neural network optimized with fruit fly optimization algorithm

As global warming becomes more prominent, the need to reduce carbon emissions to achieve China's carbon peak target is increasing. It is imperative to seek effective methods to predict carbon emissions and propose targeted emission reduction measures. In this paper, a comprehensive model integrating grey relational analysis (GRA), generalized regression neural network (GRNN) and fruit fly optimization algorithm (FOA) is constructed with carbon emission prediction as the research objective. Firstly, GRA is used for feature selection to find out the factors that have a strong influence on carbon emissions. Secondly, the parameter of GRNN is optimized using FOA algorithm to improve the prediction accuracy. The results show that (1) fossil energy consumption, population, urbanization rate and GDP are important factors affecting carbon emissions; (2) FOA-GRNN outperforms GRNN and back propagation neural network (BPNN), verifying the effectiveness of FOA-GRNN model for CO2 emission prediction. Finally, by analyzing the key influencing factors and combining scenario analysis with forecasting algorithms, the carbon emission trends in China for 2020-2035 are forecasted. The results can provide guidance for policy makers to set reasonable carbon emission reduction targets and adopt corresponding energy saving and emission reduction measures.

Impact assessment of vehicle electrification pathways on emissions of CO2 and air pollution in Xi'an, China

To assess the environmental impact of promoting the use of electric vehicles in road traffic on emissions of CO₂ and air pollution in Xi'an, China, both the proportion of electric vehicles and the power generation mix should be considered. Here, vehicle ownership in 2021 served as the baseline scenario, and the vehicle development trend through 2035 was projected. Using emission factor models for fuel vehicles and the electricity generation required for running electric vehicles, this study estimated the related pollutants' emission inventories at 81 corresponding scenarios, in which differing vehicle electrification paths were coupled with power generation mix. Further, the degree to which different vehicle electrification paths impacted the CO₂ and air pollutant emissions was also evaluated. The results show that, to achieve the goal of peak carbon emission in the road transport sector in Xi'an by 2030, the penetration rate of electric vehicles must reach at least 40 % in 2035, and the thermal power generation rate should satisfy the necessary coupling conditions. Although reducing the thermal power generation rate could mitigate the environmental problems, we find that electric vehicle development in Xi'an during 2021-2035 would still exacerbate SO₂ emissions despite reducing the thermal power generation rate to 10 %. Finally, to avoid exacerbating the adverse effects on public health from vehicle-related pollutants, the penetration rate of electric vehicles should be at least 40 % in 2035, at which time for the 40 %, 50 %, 60 %, and 70 % scenarios, the corresponding thermal power generation rate should not exceed 10 %, 30 %, 50 %, and 60 %. This study systematically analyzed plausible development paths of electric vehicles from the perspectives of peak carbon emissions, air pollution control, and human health, whose findings can serve as a timely and valuable reference for reducing pollution and carbon in the field of road transport.

Environmental impact of adult incontinence products in China in the context of population aging

With the rise of the aging population, the demand for adult incontinence products keeps increasing in developing countries. The increasing market demand for adult incontinence products will inevitably drive up upstream production, leading to more resources and energy consumption, carbon emissions, and heavier environmental pollution. It is imperative to explore the environmental impact of those products and seek opportunities to reduce the environmental impact, which is insufficient. This study aims to fill the gap in research on comparative analysis of energy consumption, carbon emissions, and the environmental impact of adult incontinence products from a life cycle perspective under different energy saving and emission reduction scenarios in the context of aging in China. Based on empirical data from a top papermaking manufacturer in China, this study applies Life Cycle Assessment (LCA) method to analyze the impact of adult incontinence products from cradle to grave. Then different scenarios in the future are set to explore the potential of and the possible pathways for energy-saving and emission-reduction of adult incontinence products from the perspective of the whole life cycle. The results indicate that the energy and materials inputs are the environmental hotspots of adult incontinence products. Under the future trend of the aging population, the predictable optimization of energy structure, optimization of material composition, and final disposal methods are far from being able to cope with the enormous environmental burden brought by the increase in the consumption of adult incontinence products, especially in 2060 which shows 3.33 to 18.40 times the annual environmental burden under the optimal energy saving and emission reduction scenario, compared to the base year 2020. Research on new environmentally friendly materials and recycling technology should be the focus of the technological development of adult incontinence products.

Simulation of future land use/cover change (LUCC) in typical watersheds of arid regions under multiple scenarios

The rapid development of the social economy has promoted a continuous increase in the intensity and scale of land use by humans, which has seriously affected the sustainable development of the region. It is important to understand the land use/cover change (LUCC) in the arid region and its future development trends and to make reasonable planning recommendations for the sustainable development of the ecological environment. This study validates the patch-generating land use simulation (PLUS) model in a typical arid region, the Shiyang River Basin (SRB), and analyzes the applicability of the model in arid regions. On this basis, the PLUS model is combined with the scenario analysis method to design four scenarios including no policy intervention, farmland protection, ecological protection and sustainable development to analyze the dynamic changes in past and future land use in the SRB and to make corresponding planning recommendations for the development of each type of land use in the arid region. The results showed that the PLUS model had a better simulation effect in the SRB (its overall accuracy reached 0.97). Coupled models obtain better simulation results than quantitative and spatial models by comparing the mainstream models, with PLUS model that combines CA model and patch generation strategy showing better simulation results in the same category. From 1987 to 2017, the spatial centroid of each LUCC in the SRB moved to varying degrees due to a continuous increase in human activities. The spatial centroid of water bodies had the most obvious change, with a moving speed of 1.49 km/a, while the moving speed of built-up land increased year by year. The spatial centroid of farmland, built-up land and unused land all shifted toward the middle and lower plains, which is a further indication of increased human activity. Due to different government policies, the development trend of land use was also different under different scenarios. However, the four scenarios all showed that the area of built-up land will be increasing exponentially from 2017 to 2037, which would seriously affect the surrounding ecological land and have a negative impact on the local agro-ecological environment. Therefore, we proposed the following planning recommendations: (1) Land leveling work should be carried out on scattered farmland located at high altitudes and with slopes over 25°. Additionally, the land use of low-altitude areas should strictly adhere to basic farmland, increase the diversification of cropping patterns and improve the efficiency of agricultural water. (2) The relationship between ecology, farmland and cities should be reasonably coordinated and the existing idle built-up land should be efficiently used. (3) Forestland and grassland resources should be strictly protected and the ecological redline should be strictly observed. This study can provide new ideas for LUCC modeling and prediction in other parts of the world and provide a strong basis for ecological management and sustainable development in arid areas.

Can China achieve its 2030 and 2060 CO2 commitments? Scenario analysis based on the integration of LEAP model with LMDI decomposition

China's ambitious targets of peaking its Carbon dioxide (CO₂) emissions on or before 2030 and achieving carbon neutrality by 2060 have been a topic of discussion in the international community. This study innovatively combines the logarithmic mean Divisia index (LMDI) decomposition method and the long-range energy alternatives planning (LEAP) model to quantitatively evaluate the CO₂ emissions from energy consumption in China from 2000 to 2060. Using the Shared Socioeconomic Pathways (SSPs) framework, the study designs five scenarios to explore the impact of different development pathways on energy consumption and related carbon emissions. The LEAP model scenarios are based on the result of LMDI decomposition, which identifies the key influencing factors on CO₂ emissions. The empirical findings of this study demonstrate that the energy intensity effect is the primary factor of the 14.7 % reduction in CO₂ emissions observed in China from 2000 to 2020. Conversely, the economic development level effect has been the driving factor behind the increase of 50.4 % in CO₂ emissions. Additionally, the urbanization effect has contributed 24.7 % to the overall change in CO₂ emissions during the same period. Furthermore, the study investigates potential future trajectories of CO₂ emissions in China up to 2060, based on various scenarios. The results suggest that, under the SSP1 scenarios. China's CO₂ emissions would peak in 2023 and achieve carbon neutrality by 2060. However, under the SSP4 scenarios, emissions are expected to peak in 2028, and China would need to eliminate approximately 2000 Mt of additional CO₂ emissions to reach carbon neutrality. In other scenarios, China is projected to be unable to meet the carbon peak and carbon neutrality goals. The conclusions drawn from this study offer valuable insights for potential policy adjustments to ensure that China could fulfill its commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060.

Effects of extreme events on land-use-related decisions of farmers in Eastern Austria: the role of learning

European farm households will face increasingly challenging conditions in the coming decades due to climate change, as the frequency and severity of extreme weather events rise. This study assesses the complex interrelations between external framework conditions such as climate change or adjustments in the agricultural price and subsidy schemes with farmers' decision-making. As social aspects remain understudied drivers for agricultural decisions, we also consider value-based characteristics of farmers as internal factors relevant for decision-making. We integrate individual learning as response to extreme weather events into an agent-based model that simulates farmers' decision-making. We applied the model to a region in Eastern Austria that already experiences water scarcity and increasing drought risk from climate change and simulated three future scenarios to compare the effects of changes in socio-economic and climatic conditions. In a cross-comparison, we then investigated how farmers can navigate these changes through individual adaptation. The agricultural trajectories project a decline of active farms between -27 and -37% accompanied by a reduction of agricultural area between -20 and -30% until 2053. The results show that regardless of the scenario conditions, adaptation through learning moderates the decline in the number of active farms and farmland compared to scenarios without adaptive learning. However, adaptation increases the workload of farmers. This highlights the need for labor support for farms.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13593-023-00890-z.

Toward sustainable reprocessing and valorization of sulfidic copper tailings: Scenarios and prospective LCA

There has been increasing attention recently to reprocessing of mining waste, which aims to recover potentially valuable materials such as metals and other byproducts from untapped resources. Mining waste valorization may offer environmental advantages over traditional make-waste-dispose approaches. However, a quantitative environmental assessment for large-scale reprocessing, accounting for future trends and a broad set of environmental indicators, is still lacking. This article assesses the life cycle impacts and resource recovery potential associated with alternative waste management through mine tailings reprocessing at a regional scale. Sulfidic copper tailings in the EU were selected as a case study. We perform prospective life cycle assessments of future reprocessing scenarios by considering emerging resource recovery technologies, market supply & demand forecasts, and energy system changes. We find that some reprocessing and valorization technologies in future scenarios may have reduction potentials for multiple impact indicators. However, results for indicators such as climate change and energy-related impacts suggest that specific scenarios perform sub-optimally due to energy/resource-intensive processes. The environmental performance of reprocessing of tailings is influenced by technology routes, secondary material market penetration, and choices of displaced products. The trade-off between climate change and energy related impacts, on the one hand, and toxicity impacts, on the other hand, requires critical appraisal by decision makers when promoting alternative tailings reprocessing. Implementing value recovery strategies for building material production, can save up to 3 Mt. CO₂-eq in 2050 compared to business as usual, helping the copper sector mitigate climate impacts. Additional climate mitigation efforts in demand-side management are needed though to achieve the 1.5 °C climate target. This work provides a scientific basis for decision-making toward more sustainable reprocessing and valorization of sulfidic tailings.

Carbon peak forecast and low carbon policy choice of transportation industry in China: scenario prediction based on STIRPAT model

As the second largest CO₂ emission department, transportation industry's carbon peak and carbon reduction are very important for China to smoothly achieve carbon peak by 2030 and carbon neutrality by 2060. This paper analyzes the influencing factors from the perspectives of population, economy, technology and transportation equipment structure, subdivides 20 scenarios to predict the carbon emissions of the transportation industry of the whole China and various regions based on scenario analysis method, explores the carbon peak path, and puts forward corresponding policy recommendations. The study found that (1) from the overall trend of carbon emissions, the total carbon emissions of China's transportation industry showed an overall upward trend from 2010 to 2019 while the growth rate of carbon emissions showed a downward trend. (2) From the perspective of influencing factors, population size, urbanization rate, economic scale, traffic development, traffic carbon intensity, and highway mileage have positive effect on the growth rate of China's transportation CO₂ emissions. The increase in the proportion of energy structure and railway cargo turnover has the negative effect on carbon emissions in the transportation industry. (3) From the prediction results at the national level, technological breakthroughs have a limited effect on carbon emission reduction in China's transportation industry, while structural equipment optimization has the most significant effect on its emission reduction. When technological breakthroughs and equipment structure optimization are carried out simultaneously, the carbon emission reduction effect is the best. The carbon peak of China's transportation industry would achieve as early as 2030, with a peak range of 70,355.54-84,136.17 million tons. (4) From the perspective of prediction results at the regional level, the provinces with rapid population growth and per capita GDP growth, the provinces with rapid population growth and per capita GDP growth, and the provinces with low population growth and per capita GDP growth should control their average annual growth rate of carbon emissions of the transportation industry to 1.13%, 0.72% and 0.58% respectively in 2019-2030, in order to ensure the achievements of the carbon peak target.

Potential pathways to reach energy-related CO2 emission peak in China: analysis of different scenarios

The prevalence of global unilateralism and the shock of COVID-19 brought considerable uncertainty to China's economic development. Consequently, policy selection related to the economy, industry, and technology is expected to significantly impact China's national economic potential and carbon emission mitigation. This study used a bottom-up energy model to assess the future energy consumption and CO₂ emission trend before 2035 under three scenarios: a high-investment scenario (HIS), a medium-growth scenario (MGS), and an innovation-driven scenario (IDS). These were also used to predict the energy consumption and CO₂ emission trend for the final sectors and calculate each sector's mitigation contribution. The main findings were as follows. Firstly, under HIS, China would achieve its carbon peak in 2030, with 12.0 Gt CO₂. Moderately lowering the economic growth rate to support the low-carbon transition of the economy by boosting the development of the low-carbon industry and speeding up the employment of key low-carbon technologies to improve energy efficiency and optimize energy structure in the final sectors, the MGS and the IDS would achieve carbon peak approximately in 2025, with a peak of 10.7 Gt CO₂ for the MGS and 10.0 Gt CO₂ for the IDS. Several policy recommendations were proposed to meet China's nationally determined contribution targets: instigating more active development goals for each sector to implement the "1+N" policy system, taking measures to accelerate the R&D, boosting the innovation and application of key low-carbon technologies, strengthening economic incentives, forming an endogenous driving force for market-oriented emission reduction, and assessing the climate impacts of new infrastructure projects.

Regional allocation of carbon emission quotas in China under the total control target

The allocation of provincial carbon emission quotas under total amount control is an effective way for China to achieve its carbon peak and neutrality targets. Firstly, in order to study the factors influencing China's carbon emissions, the expanded STIRPAT model was constructed; and combined with the scenario analysis method, the total of national carbon emission quota under the peak scenario was predicted. Then, the index system of regional carbon quota allocation is constructed based on the principles of equity, efficiency, feasibility, and sustainability; and the allocation weight is determined by the grey correlation analysis method. Finally, the total carbon emission quota under the peak scenario is distributed in 30 provinces of China, and the future carbon emission space is also analyzed. The results show that: (1) only under the low-carbon development scenario, can China reach the peak target by 2030, with a peak carbon of about 14,080.31 million tons; (2) under the comprehensive allocation principle, China's provincial carbon quota allocation is characterized by high levels in the west and low in the east. Among them, Shanghai and Jiangsu receive fewer quotas, while Yunnan, Guangxi, and Guizhou receive more; and (3) the future carbon emission space for the entire country is modestly surplus, with regional variations. Whereas Hainan, Yunnan, and Guangxi have surpluses, Shandong, Inner Mongolia, and Liaoning have significant deficits.