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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]

Electricity demand is a crucial factor in energy system planning. Understanding future electricity demand is vital for developing effective energy and climate policies, as well as establishing a resilient and sustainable energy system. In light of these considerations, the escalating challenges posed by climate change are anticipated to have a substantial impact on electricity demand. This study, therefore, provides a comprehensive analysis delving into the dynamic nature of Temperature Response Functions (TRFs) of electricity demand across Europe. By examining various factors influencing electricity demand in residential buildings, such as thermal insulation, heating electrification, space cooling, and passive cooling, we aim to understand their collective impact on shaping future Temperature Response Functions. To project electricity demand, our study incorporates these factors into our scenario assumptions. Through a comprehensive investigation of these scenarios, our findings reveal distinctive regional influences of these factors. In regions where heating demand prevails, an initial increase in electricity demand is anticipated due to increased electrification rates. However, improved building thermal insulation is expected to substantially reduce winter electricity demand in the long run. Conversely, in regions with pronounced cooling demand, a notable escalation in electricity demand is foreseen due to increased space cooling penetration rate. Nevertheless, the application of effective passive cooling measurements is expected to mitigate and markedly diminish this increase. By highlighting the differential influences of these factors on electricity demand across Europe, our findings can offer valuable insights and guidelines first for energy system modelers for considering the change in Temperature Response Functions and second for policymakers to develop effective climate change adaptation and mitigation strategies. • Examination of the dynamic characteristics of Temperature Response Functions (TRFs). • Space and passive cooling, electric heating, insulation effects on electricity demand. • Projection of TRFs and electricity demand for 36 European countries until 2100. • Thermal insulation and passive cooling are essential for reducing electricity demand. [ABSTRACT FROM AUTHOR]

In the pursuit of sustainable development, accurate renewable energy demand forecasting holds great significance for climate change mitigation and promoting sustainability. However, renewable energy forecasting has been consistently challenged by seasonality and nonlinearity. Identifying the periodic and nonlinear characteristics concealed within renewable energy sources accurately is still an unexplored problem. Consequently, an innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting, which incorporates seasonal dummy variables and a power exponent term for handling the seasonality and nonlinear patterns in time series. Furthermore, an intelligent algorithm matching framework is proposed to augment the flexibility of the newly developed model. For practical purposes, the new methodology is contrasted against a range of benchmarks encompassing statistical, machine-learning, and traditional grey models in forecasting the quarterly total renewable energy consumption in the United States. The proposed model exhibits over 27% improvement rates over its counterparts, achieving the most superior predictive accuracies of 1.45%, 39.27, and 0.79 in MAPEP , RMSEP , and MASEP metrics, respectively. Furthermore, the probability density and sample size analyses are conducted to validate the robustness of the new model, confirming its adaptability and stability towards algorithm randomness and historical information volume. Consequently, the novel model is employed to forecast the short-to-long-terms renewable energy consumption in the U.S., showcasing an upward trend and seasonal fluctuations of the consumption for the forthcoming 24 quarters from 2023Q4 to 2029Q3. These insights can offer valuable implications to the stakeholders such as energy suppliers, utility managers, and policy advocates, highlighting actionable strategies for optimizing renewable energy consumption forecasting and aiding sustainable development initiatives. • An innovative nonlinear discrete seasonal grey model is proposed for renewable energy forecasting. • The optimum algorithm matching the novel model is determined from five alternatives. • The Monte Carlo Simulation and parameter sensitivity analysis advance the robustness. • PSO with the optimal parameter group has been selected for searching coefficients. • The new model possesses higher accuracy and robustness than various competitors. [ABSTRACT FROM AUTHOR]

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]

Power pooling has emerged as a regional strategy for accelerating generation capacity expansion in West Africa with the aim of leveraging vast domestic energy resources and promoting investment in regional power infrastructure. As part of their climate action pledges, most West African countries have committed to increasing the shares of variable renewable energy (VRE), particularly solar photovoltaic and wind power, in their generation mix. It, however, appears that approaches to utility-scale capacity expansion planning tend to overlook the inherent temporal intermittency and spatial variability of VRE-based generation. Moreover, despite influencing the techno-economic rationale for grid expansion and off-grid electrification, as well as the trade-offs between high renewable supply areas and grid expansion, the spatial distribution of demand has been overlooked in planning approaches, leading to conservative and weak prospects for the contribution of utility-scale VRE. Such inconsistencies with the region-wide potentials and policy ambitions highlight that it is paramount for West Africa to design its power pooling such that spatial and temporal fluctuations of VRE supply are duly considered in capacity expansion planning while taking advantage of complementarities between VRE supply and national electricity demand profiles. To address this, the present paper applies a long-term generation capacity expansion model, OptGen, soft-linked to a least-cost operation module, the SDDP tool, which is enhanced by geospatial electrification analysis using the OpeN Source Spatial Electrification Tool (OnSSET), to a subset of four member countries of the West African Power Pool – Burkina Faso, Côte d'Ivoire, Ghana, and Mali – for the 2023–2040 time horizon. The results highlight that current frameworks lead to missed opportunities for bridging the supply-demand gap in all countries, not only in terms of VRE generation capacity, but also of cross-border power trade. • Long-term capacity expansion planning and dispatch models are soft-linked. • The spatial and hourly distributions of demand and renewables supply are considered. • The 10% limit on renewables share used in the regional master plan is not optimal. • The current system operation is not conducive to optimal cross-border trade. • The relaxed transmission scenario yields higher dispatch factors for renewables. [ABSTRACT FROM AUTHOR]

The 2015 Sustainable Development Goals (SDGs) provide a set of aspirational objectives for the world, addressing interlinked themes including poverty, hunger, and climate action. Meeting these goals requires a similarly integrated and interdisciplinary approach. One component of such an approach may be agrivoltaic systems (AVS): dual use solar and agricultural systems wherein crops are grown beneath and between solar panels. Given their intersectoral nature, AVS will have complex impacts on the achievement of the SDGs. This analysis seeks to evaluate how AVS could impact progress towards the SDGs based on literature around the impacts of agriculture, solar, and AVS, and the indicators measuring progress towards the SDGs. It uses a Strengths, Weaknesses, Opportunities, and Threats (SWOT) framework and applies it in a unique way to understand the direct and immediate impacts to the SDGs following the installation of an AVS (i.e., strengths and weaknesses), and the potential impacts on the SDGs based on secondary or supplementary actions taken once an AVS is installed (i.e., opportunities and threats). Though the most obvious of AVS' impacts to the SDGs will be on Goals 2 (Zero Hunger) and 7 (Affordable and Clean Energy), evaluation of individual indicators shows that AVS can quantifiably impact 14 out of 17 SDGs, largely in a positive manner. However, evaluating impacts on the SDGs indicator by indicator also reveals possible negative, and unexpected, consequences of AVS. Yet, impacts on the SDGs will vary by location-specific factors such as climate, and so this analysis concludes with an application of the SWOT framework in Ethiopia for a farm where an AVS is under consideration. Altogether, this work should provide a means for evaluating the impacts of AVS on SDGs and insights into how to mitigate possible detrimental impacts while capitalizing on benefits. • Agrivoltaics measurably impact 13 out of the 17 Sustainable Development Goals. • Impacts of agrivoltaics depend on a reference case: single use agriculture or solar. • Agrivoltaic systems can underpin rural electrification of farms and schools. • Targeted government initiatives can capitalize on the benefits of agrivoltaics. [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]

Direct air capture (DAC) technology plays a crucial role in mitigating the effects of global warming by capturing carbon dioxide (CO 2) directly from the atmosphere. A significant technical challenge for DAC is its high thermal energy consumption, which results in an unacceptable overall cost (600 $ t CO 2 – 1). The combination of a high-temperature heat pump (HTHP) and DAC system offers a substantial reduction in thermal energy consumption, decreased reliance on renewable energy sources, and enhanced DAC system flexibility. This study presents three distinct thermal integration strategies that combine an HTHP with solid adsorbent-based DAC and verifies their effectiveness in reducing energy consumption and CO 2 emissions compared to a traditional DAC system. Among them, the deeply integrated DAC-HTHP system (I-DAC) exploits low-grade adsorption heat and waste heat, providing direct heating and cooling energy to the DAC system via refrigerant condensation and evaporation processes. Simulation results indicate that the I-DAC system achieves an extremely low operating energy consumption of 2.77 GJ t CO 2 – 1 , representing a 69.5% reduction than traditional DAC under the same conditions. Consequently, the I-DAC offers a cost-effective (32.0–46.2 $ t CO 2 – 1) negative emission solution. We also demonstrate that the I-DAC is promising for wide application in cities to recycle waste heat and remove CO 2 from the air. The comprehensive implementation of the I-DAC in 105 cities could yield a net annual productivity of 980 MtCO 2. • Proposed a deeply thermal integrated DAC system (I-DAC) with waste heat recovery heat pump. • I-DAC achieved a 69.5% reduction in specific energy consumption compared to base-DAC. • I-DAC offers a cost-effective and scalable negative emission solution for climate change mitigation. • Successful implementation of I-DAC in 105 cities could yield a net annual capture capacity of 980 MtCO 2 by 2050. • Highlights the potential of I-DAC to recycle waste heat and remove CO 2 from the air, with policy implications [ABSTRACT FROM AUTHOR]