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We perform an ex-ante life cycle assessment, integrating cradle-to-gate greenhouse gas emissions into an off-grid energy system model. By applying a multi-objective optimization, minimizing both costs and carbon dioxide equivalent (CO 2 e) emissions, we find the Pareto boundary between these two goals. As a case study, we chose the power supply to an astronomical observatory in Chile, using mostly solar power and energy storage in batteries and hydrogen. We compare the ex-ante study to a prior ex-post life cycle assessment, and furthermore dive into sensitivities of our model regarding component lifetimes and costs. We find (i) low-hanging fruit in lowering emissions with small cost increases, (ii) ex-ante life cycle assessments' possibility to find less CO 2 e intensive power systems compared to ex-post conducted studies, (iii) a pronounced sensitivity of the optimization model on assumed lifetimes of energy storage components. This study shows the importance of including life cycle CO 2 e emissions into the optimization objective of energy system models. This method uncovers the environmental and economic trade-offs associated with high shares of renewable energies in off-grid energy systems. • We integrate life cycle assessment (LCA) into an off-grid energy system optimization. • The applied case study powers a remote astronomical telescope in Chile. • A multi-objective optimization finds Pareto curves at varying costs and lifetimes. • Low-hanging fruit shows low additional system costs to reduce CO 2 e impacts. • In contrast, a separate LCA did not find such trade-offs in costs and CO 2 e emissions. [ABSTRACT FROM AUTHOR]

While identifying the drivers of global CO 2 emission is crucial for climate change mitigation, the heterogeneous technology-related drivers (e.g., technological change and efficiency of energy and CO 2 emission) were ignored to a large extent at a global scale, hindering to formulate heterogenous climate policies. Moreover, the projection of CO 2 emission was also not well compared for countries. Here, the study investigated the heterogeneous technology-related drivers of CO 2 emissions in time and space simultaneously in 42 major emitter countries over 1998–2020 by extending the spatiotemporal production-theoretical decomposition models, and compared the different performances for forecasting CO 2 emission by traditional time-series models and several machine learning models. Key findings as follows: first, drivers of CO 2 emissions exhibit significant heterogeneity across countries where the effects of energy usage technology gap and CO 2 emission technology gap were negative drivers for USA, South Korea, and the Czech Republic and potential energy intensity effect was the negative driver in countries like China, Russia, Japan, and India. Second, the effects of within-GDP per capita and within- population size were the important drivers affecting global CO 2 emission difference. Third, general regression neural network achieved the best forecasting performance on average compared with other models in the study. The study highlights the importance of formulating climate policies based on heterogeneous technology and emission forecast modeling. • Heterogeneous technology-induced global CO 2 emission reduction was analyzed. • Two spatial production-theoretical decomposition models were proposed. • Forecasting models including machine learning were compared for projecting. [ABSTRACT FROM AUTHOR]

The ever-increasing challenges related to water security and climate mitigation underscore the pressing need for forward-thinking systems planning tools to drive urban sustainability transitions. In this study, we propose a novel city-level multi-objective planning framework that explores the nexus between carbon trading and water management, with the aim of facilitating water-efficient urban industrial restructuring. The framework integrates cap-and-trade mechanisms into a two-layered optimization model, creating an integrated approach to optimizing carbon emissions and generating economic opportunities for improving water efficiency. To illustrate the potential application of this framework, we conducted a case study focusing on Dongguan City, a water-stressed industrial metropolis in southern China. The results illustrate potential synergies between water planning strategies and carbon trading schemes, which could be harnessed to enable targeted reductions in water usage and carbon emissions. Furthermore, our findings identify the textile, apparel, and paper products manufacturing industries as primary candidates for strategic production scale reduction, emphasizing the importance of sustained support for the development of the computer and electronic manufacturing sector as a catalyst for urban sustainability transitions. By pioneering this new nexus-based perspective, our study offers valuable insights into long-term strategic planning for a low-carbon and resource-efficient urban economy. • Synergizing carbon trading and water management within a novel integrated planning framework. • Implementing resource-efficiency-driven optimization for water- and carbon-intensive manufacturing sectors. • Gaining insights into the specific roles of key manufacturing sectors in water management and carbon trading. • Introducing carbon trading systems may help in promoting water-efficient industrial restructuring. [Display omitted] [ABSTRACT FROM AUTHOR]

The Chinese government has announced ambitious carbon reduction goals to address climate change, however, there has been limited scientific attention to the achievement of deep decarbonization in regions with insufficient economic development. Moreover, the complex impacts of climate change mitigation on energy system, environment and economic development remain unclear in Northwest China. Here we construct a comprehensive evaluation model system to evaluate the effects of different climate mitigation pathways in Qinghai, Ningxia, Gansu and Xinjiang provinces. Results show that the four provinces need to vigorously increase the share of electricity that consumed in final energy to 53.1–60.0% and 52.9–62.0% for 2 °C and 1.5 °C target, respectively, and reduce total energy demand to 11.6–44.4 Mtoe and 8.2–34.1 Mtoe in 2050. Meanwhile, the CO 2 emissions for their energy systems will reach peak by 2025 at lower values. Moreover, the synergies of CO 2 reduction are most conducive to abating SO 2 pollution and also contributing to decreasing PM 2.5 concentrations. Additionally, the GDP losses in 2050 fluctuate from 5.0 to 21.7 billion USD in the 2 °C scenario to 11.5–52.7 billion USD in the 1.5 °C scenario. Our study provides practical guidance on achieving carbon reductions in less developed regions of China and lays foundations to promote decarbonization of energy systems in other provinces. • Realizing climate goals requires low-carbon energy systems led by electricity. • Energy-related carbon emissions in Northwest China will peak by 2025. • More ambitious climate and clean air policies are needed for air quality improvement. • Economic loss shows an upward trend in the future under climate targets. [ABSTRACT FROM AUTHOR]

Efforts to avoid the acceleration of global warming have tended to focus on countries with high CO 2 emissions levels and large populations, with a high level of economic development or industrialization. African countries, which often do not conform to such criteria, are more vulnerable to climate change due to their dependence on climate-sensitive industries and their limited infrastructure and technological capacity to cope with its impacts. The long-term economic growth rates projected for Africa's rapid development period will, further, make Africa a potential emission hotspot in the near future. Here, for the first time, we built an energy-related emissions inventory for 19 African countries for 2010–2019, which addresses emissions from 47 economic sectors and 5 energy types, making it the most comprehensive of its kind. The degree of decoupling of economy and emissions, and drivers of CO 2 emission changes are also examined. Most African countries experienced rapid growth in CO 2 emissions, with an average annual growth rate of 5.5% for fossil fuel-related CO 2 emissions and 6.0% for unsustainable biomass-related CO 2 emissions. Only two countries, South Africa and Tanzania, have achieved a strong decoupling of economic growth from CO 2 emissions. Economic and population are the most important drivers of emissions, while energy intensity has been identified as a key factor in mitigating CO 2 emissions, especially for those countries that have reached strong or weak decoupling. The findings from this study provide essential insights that could guide the development of low-carbon policies and strategies in Africa. • We provide energy-related emission inventories for 19 African countries from 2010 to 2019. • African countries experienced rapid growth in CO2 emissions. • Two countries achieved strong decoupling of GDP from CO2 emissions. • Economic and population growth are the most important drivers of emissions. [ABSTRACT FROM AUTHOR]

Decarbonizing the passenger transportation sector is critical for climate change mitigation. Existing studies on net-zero scenarios using Energy System Optimization Models (ESOM) often overlook non-monetary aspects of consumers' mobility choices but primarily focus on cost aspects. This study incorporates consumers' travel time duration and valuation, and an endogenous modal shift option into the Swiss TIMES Energy system Model (STEM). STEM is applied in a multi-objective optimization framework to quantify the impacts of faster Public Transport (PT) and slower car speeds on modal shifts in the transport sector's transformation. Similarly, we assess scenarios where consumers weigh travel time less, reflecting improved travel productivity. The results show that speed variations on medium- and long-distance trips, which can be interpreted as policies for highway speed limits and more efficient PT, can induce modal shifts towards 5–10% higher PT demand. Its implied secondary effects across the energy system include a reduced need for electrification of heavy-duty trucks by 11% and a decrease in hydrogen demand in road transportation by 34% by 2050. If travelers weigh costs over travel time, PT becomes less competitive against cars. Thus, electric vehicles (EVs) need to play a more dominant role in decarbonization, with a demand increase of 13% in 2040 (+9.2 billion passenger kilometer (bpkm)) and 6% in 2050 (+5.0 bpkm), along with the need for additional 45,000 public chargers of 22 kW size. Policy implications include the emphasis on improved PT speeds, speed limits on highways, needs to achieve more widespread EV adoption, and the need for balancing travelers' decision factors when aiming for reduced transport CO 2 emissions. • Non-monetary aspects relevant for modal shift incorporated in the Swiss TIMES model. • Multi-objective optimization to quantify net-zero impacts of non-monetary aspects. • Travel speed measures induce a 5–10% demand uptake of public transport. • Weighing travel time less makes electric cars more critical for decarbonization. • Secondary energy system effects are quantified & policy implications are drawn. [ABSTRACT FROM AUTHOR]

• BLSVM-MFA method is developed for predicting renewable power generation potential. • BLSVM-MFA can reflect nonlinear relationship between CETC factors and WPG & PPG. • Main factors affecting WPG and PPG and their contributions can be identified. • Ensemble predictions reveal that Xiamen's WPG and PPG potentials decrease. • Results are valuable for energy-structure transformation and carbon abatement. In order to reduce fossil-energy carbon emission and mitigate its climate change impact, developing integrated mathematical methods that are capable of predicting renewable power-generation (e.g., wind and photovoltaic power) potential are desired. This study advances an integrated Bayesian least-squares-support-vector-machine based multilevel-factorial-analysis (BLSVM-MFA) method to find out the main factors affecting wind- and photovoltaic-power generations (WPG and PPG), as well as predict WPG and PPG potentials. The BLSVM-MFA method is then applied to a case study of Xiamen (China), where various factors including climate, economic, technological and capacity are investigated and 216 scenarios are analyzed. Results disclose that the main factors affecting WPG are wind power index (contributing 54.04%) > installed capacity (41.45%) > technology (2.60%), and the main factors affecting PPG are installed capacity (60.36%) > solar radiation (19.27%) > technology (9.24%). Results also reveal that WPG and PPG potentials (in 2020–2035) decrease under most scenarios and WPG and PPG show seasonal complementarity. Ensemble predictions under 216 scenarios indicate that WPG and PPG would be 130.21 WMh and 436.51 MWh by 2035. The promotion of high-conversion and high-profit technologies can improve the competitiveness of WPG and PPG in the regional power system. Results are valuable for revealing the impacts of climate change, economic development, and technological improvement on renewable power potentials and providing support for regional energy-structure transformation and carbon-emission abatement. [ABSTRACT FROM AUTHOR]

• Energy use positively affects carbon emission intensity. • Urbanization has positive impact on carbon emission intensity. • Technological innovation negatively affects carbon emission intensity. • Data was analyzed by Quantile-on-quantile model. In recent years, policy experts have engaged in a spirited discussion over the relationship between environmental quality and energy use. This association is critical for the mitigation of climate change and achieving Sustainable Development Goals (SDGs). Furthermore, current studies have not given enough consideration to this relation, notably in the case of the Kingdom of Saudi Arabia (KSA). Therefore, this study examines the KSA case, focuses on the connection of industrialization, foreign direct investments, and technological innovation with carbon emissions intensity (CEI) by also considering urbanization and energy use, examines the period between 1991 and 2020, and uses quantile-on-quantile (QQ) regressionand quantile regression (QR) approaches for empirical examination. The empirical outcomes revealthat (i) urbanization and energy usehave an increasing impact on CEIin all quantiles; (ii) industrialization, foreign direct investments, and technological innovation have a generally decreasing impact on the CEI in most quantiles; (iii) power of the factors' impact on the CEI change according to quantiles; (iv) the robustness the findings is also validated. Hence, theresults highlight the importance of quantile-based analysis and suggest that KSA authorities need to embrace environment-friendly policies while managing environmental pollution to develop industrialization level and technological innovation as well as increase foreign direct investments. [ABSTRACT FROM AUTHOR]

Infrastructure for heat provision in the built environment needs to change remarkably to support lowering carbon emissions and achieving climate mitigation targets before 2050. We propose a computational approach for finding a mix of heat options per neighbourhood that minimises cumulative carbon emissions between 2030 and 2050, referred to as committed emissions, while at the same time adhering to technological constraints at both the household and neighbourhood scales. To establish this approach, we integrated bottom-up heat demand modelling at neighbourhood scale with a mixed-integer non-linear optimisation problem. Nine scenarios with different pathways for the insulation of buildings and the decarbonisation in electricity generation were considered and applied to three neighbourhoods in the city of Amsterdam, the Netherlands. The results show that (i) the committed emissions are ten times lower between 2030 and 2050 in scenarios in which ambitious measures are taken for the insulation of buildings and the decarbonisation in electricity generation, (ii) only in these 'ambitious scenarios' low temperature heat systems, such as heat pumps and low temperature heat networks, are optimal solutions for minimising committed emissions, (iii) if less ambitious insulation and decarbonisation measures are taken, high temperature heat options can be part of the heat mix with lowest committed emissions, and (iv) the minimum heat density for low temperature heat networks is not always achieved, creating risks for carbon lock-ins when applying these heat networks. Our results clearly indicate that pathways for the retrofitting of buildings and the decarbonisation in electricity generation need to be taken into account jointly when designing renewable and low-carbon heat systems to optimally reduce carbon emissions towards 2050 and reduce future carbon lock-ins. • Bottom-up heat demand models enable testing spatial heat system requirements. • Power supply decarbonisation and building insulation characterise ambitious policies. • Hybrid or low temperature heat systems are optimal solutions for ambitious policies. • Ambitious decarbonisation policies can lower committed emissions tenfold. • Heat networks can cause a carbon lock-in due to the needed minimum heat density. [ABSTRACT FROM AUTHOR]

• Pursuing a zero-carbon US electricity system would incur additional cost. • The use of non-renewable low-carbon resources reduces the additional cost. • Negative emission technologies show economic advantage to mitigate the last few percent CO 2 from the US electricity system. Decarbonizing the electricity system to zero-carbon emission is crucial for climate change mitigation. Previous studies have shown that such a transition in the United States (U.S.) may lead to higher system cost compared to a business-as-usual case, but it is not well-known how the cost of electricity generation varies at sub-regional level under the transition, and studies have rarely evaluated the trade-off between the cost and avoided climate damages, as well as the potential roles of negative emission technologies (NETs) in the electricity decarbonization. Here, we present a regionally resolved national model to quantify the cost of decarbonizing the U.S. electricity system under a set of possible scenarios. The results show that, compared to the reference scenario without a decarbonization policy, reaching zero CO 2 emission by 2050 would incur, depending on the scenarios, 335–494 billion USD additional cost to the U.S. electric power sector during 2020–2050. The regional costs of electricity generation ranges from 2.4 to 4.7 cent/kWh, largely due to the generation profiles and renewable resources availability of those regions. The additional costs can be translated to an average CO 2 abatement cost of 29–59 USD/metric ton CO 2 (with 2%–7% discount rates), which are comparable to the social cost of carbon in the literature at around 4% discount rate. The results also show that the cost of mitigating the last few percent CO 2 emission from the U.S. electricity system may exceed the costs of NETs, indicating an opportunity for NETs to contribute to electricity decarbonization. [ABSTRACT FROM AUTHOR]

• Hydrogen accounts for 5–10% of total energy in 2050 under 2 °C scenarios. • Hydrogen share increases for a 1.5 °C goal and reaches 15% if CCS is limited. • Transport is the largest consumer of hydrogen, followed by industry and power. • Synthetic hydrocarbons based on hydrogen and direct air capture are an option. • Holistic energy policy design is needed rather than heavy dependence on hydrogen. Hydrogen-based energy carriers, including hydrogen, ammonia and synthetic hydrocarbons, are expected to help reduce residual carbon dioxide emissions in the context of the Paris Agreement goals, although their potential has not yet been fully clarified in light of their competitiveness and complementarity with other mitigation options such as electricity, biofuels and carbon capture and storage (CCS). This study aimed to explore the role of hydrogen in the global energy system under various mitigation scenarios and technology portfolios using a detailed energy system model that considers various energy technologies including the conversion and use of hydrogen-based energy carriers. The results indicate that the share of hydrogen-based energy carriers generally remains less than 5% of global final energy demand by 2050 in the 2 °C scenarios. Nevertheless, such carriers contribute to removal of residual emissions from the industry and transport sectors under specific conditions. Their share increases to 10–15% under stringent mitigation scenarios corresponding to 1.5 °C warming and scenarios without CCS. The transport sector is the largest consumer, accounting for half or more of hydrogen production, followed by the industry and power sectors. In addition to direct usage of hydrogen and ammonia, synthetic hydrocarbons converted from hydrogen and carbon captured from biomass or direct air capture are attractive transport fuels, growing to half of all hydrogen-based energy carriers. Upscaling of electrification and biofuels is another common cost-effective strategy, revealing the importance of holistic policy design rather than heavy reliance on hydrogen. [ABSTRACT FROM AUTHOR]

• Distribution effect of carbon-food nexus for Chinese cities were explored. • Several methods built using the spatial decoupling index were proposed. • Chinese cities exhibited varying degrees of the distribution effect over the period. • Driver pattern of carbon-food nexus has changed over the years. Climate change mitigation and food security are sustainable development goals (SDGs) of the United Nations. Despite extensive studies focused on the nexus of different SDGs and corresponding mechanisms, the distribution effect of carbon-food nexus and its driver has received limited attention, hindering policy making for balancing pathways of regional emission reduction and food production improvement. The present study explored the distribution effect (i.e., spatial performance) of carbon-food nexus from multiple perspectives (namely, status quo, drivers, and trend) based on a unique city-level dataset from 2005 to 2017. To this end, we proposed the spatial decoupling index, the corresponding decomposition and nonparametric models for capturing the multi-perspective distribution effects. The results indicated that Chinese cities, categorized by population scale and economic structure, exhibited varying degrees of distributions of carbon-food nexus over the period. Moreover, the driver pattern of carbon-food nexus was noted to have changed, wherein investment efficiency and scale were positive drivers. Furthermore, we showed that a more developed city's economy was statistically significantly associated with higher carbon emission and lower food production. Therefore, related stakeholders should take more actions such as strengthening capital accumulation and joint management of carbon intensity, for improving the distribution effect of the overall city-level carbon-food nexus. [ABSTRACT FROM AUTHOR]

[Display omitted] • A bi-level joint-probabilistic programming approach is developed. • It can handle leader–follower issues and joint-probabilistic constraints. • The proposed approach is applied to China's multi-regional energy system model. • The optimal schemes during 2021–2050 under three mitigation scenarios are obtained. • Results are helpful to achieve low-carbon goals in multi-regional energy system. Climate change mitigation and renewable resources utilization are becoming particularly urgent for energy system management. In this study, a bi-level joint-probabilistic programming (BJPP) method is developed for planning multi-regional energy system under different mitigation policies and uncertainties. BJPP can handle leader–follower issues in decision-making process as well as examine the risk of violating joint-probabilistic constraints. Based on the BJPP method, a China's multi-regional energy system (named as BJPP_CMES) model is formulated to provide optimal scheme for energy system planning of China over a long-term horizon (2021–2050) by synergistically minimizing carbon dioxide (CO 2) emission and system cost. A series of scenarios associated with different carbon capture and storage (CCS) levels and violation risks of energy-demand constraints are examined. Results reveal that: (i) the share of non-fossil energy in China's energy supply would keep increasing in 2021–2050, and the highest growth of the renewable supply would occur in Ningxia (rising 47.7%); (ii) Sichuan, Inner Mongolia, and Gansu would be the top three suppliers of renewable electricity; (iii) the CO 2 emission of China would reach a peak of [44.3, 54.8] billion tonnes during the period of 2026–2030; Shandong, Inner Mongolia, and Shanxi would be main contributors of CO 2 emission in the future; (iv) compared with the single-level model, the CO 2 emission from the BJPP_CMES model would reduce by [2.7, 5.7]%; (v) among developed regions, the individual probability level of Jiangsu-Zhejiang-Shanghai is the most significant parameter for both CO 2 emission and system cost. The findings are helpful for decision makers to optimize multi-regional energy system (MES) with a low-carbon and cost-effective manner, as well as to provide useful information for renewable energy utilization and regional sustainable development. [ABSTRACT FROM AUTHOR]

• Method was established to model building stock composition in adopted technologies. • Stock model can estimate the baseline development of building stock. • Building stock energy models can be constructed to use building stock data. • The Japanese technology deployment trend can significantly reduce CO 2 emissions. • Efficiency measures help avoid the increase in electricity demand by electrification. A significant improvement in the building stock energy efficiency is imperative to mitigate climate change. Building stock energy models (BSEMs) that employ reference building models are useful for mitigation analysis. However, most existing BSEMs developed for commercial building stock focus on limited building systems and energy conservation measures, and technology deployments are suggested based on simple vintage-driven scenarios. These approaches are insufficient, particularly for regions where improvements to building insulation performances can have a modest impact. This study aims to establish a BSEM framework to overcome this issue and to validate the framework via its application to the Japanese commercial building stock. The framework develops statistical models for estimating the selection probabilities of system alternatives and utilizes them to disaggregate building stocks. A reference building model is developed for each stock segment. The results show that this approach facilitates the use of multiple technological options considering various factors that affect technological deployments, and it also helps estimate the baseline development of building stocks. Furthermore, the developed model well represents the observed distributions in energy use intensity and estimates the aggregated energy consumption of building stocks with a reasonable accuracy. The baseline development was estimated to reduce the CO 2 emission by 18% by 2030 from 2013. Efficiency measures can help avoid the increase in electricity demand caused by electrifying the heat source of heating, ventilating, and air-conditioning and water heating systems. The framework could help extend the scope of BSEM application because BSEM development is not information intensive. [ABSTRACT FROM AUTHOR]

• Incorporate the emission inventory into an improved emission spatial mapping model. • Provide an estimate of emission characteristics and spatial distribution in cities. • Conduct the driving forces and emission reduction targets based on Paris Agreement. • Localized reduction policies are the keys to achieving low carbon city development. The identification of CO 2 emissions in megacities is vitally important to promote a transition to low-carbon city. This study aims to analyze the CO 2 emission characteristics and spatial distribution in megacities among different countries, which is important for climate change mitigation. In this study, 12 megacities from China, Japan, and South Korea were selected as typical case studies for analysis. Results show that Chinese cities' CO 2 emissions are among the top four cities studies and are much higher when compared to the other sample cities in Japan and South Korea. Chongqing, Incheon, Tianjin, and Shanghai were the top four cities with the highest carbon intensity. The spatial distribution of urban carbon emissions varies widely. In Seoul, Tokyo Metropolis, and Beijing, 90% of carbon emissions are concentrated on 74.17%, 55.95% and 8.93% of the land area, respectively. The results of the driving forces and emission reduction targets analysis indicate that the three countries face different challenges and there are different action plans in each city accordingly. This study proposed the carbon emissions reduction targets and countermeasures in different industrial sectors, including an increased rate of the standardization of city CO 2 emission accounting systems and the decarbonization of the power industry, among others. These countermeasures will not only contribute to the analysis of CO 2 emissions but will also promote to the low-carbon city development and encourage the realization of urban sustainable development goals. [ABSTRACT FROM AUTHOR]