China, the world's largest electric vehicle (EV) market, plays a pivotal role in global decarbonization of the transport sector. We present the first high-resolution assessment of EV adoption in 295 cities, utilizing more than 20 million registrations of 586 EV models tracked monthly from 2022 to 2024 and projecting transition pathways to 2035. Real-world data reveal that EVs are 30.9-212.8 MJ per 100 km more energy efficient than internal combustion vehicles, yet their carbon intensities range from 18.2 to 270.4 gCO2 /km among provinces. The limited electrification of hybrids means that gasoline still accounts for 44% of EV energy use. Scenario projections suggest that emissions will peak about 2030 at 21.1-30.9 megatonnes of CO2 and decline by 2035 under continued market transition. The findings establish an empirical foundation for accurate emissions accounting, emphasize the need to reduce regional disparities in adoptability, and offer globally relevant insights for road-transport decarbonization.

The continuous growth of China's private passenger vehicle fleet has intensified material demand and embodied carbon emissions, underscoring the need for effective decarbonization pathways. This study develops a transferable, dynamic material flow analysis framework to assess vehicle stocks, metal flows (steel, aluminum, and copper), and embodied emissions from 2000 to 2070, and to quantify the contributions of demand-side and technology-side efficiency measures. The results reveal that: (1) The vehicle fleet is projected to peak at 327-507 million vehicles by mid-century, with new energy vehicles dominating both in-use stocks and end-of-life flows by the 2040s. (2) Cumulative metal demand is projected to reach 1914-2990 million tonnes over the upcoming five decades, with 879-1320 million tonnes supplied from secondary sources under baseline conditions. Technologyoriented measures substantially enhance recycling performance, enabling secondary steel to fully meet manufacturing demand and allowing aluminum and copper cycles to approach near closure by 2070. (3) Correspondingly, cumulative embodied carbon emissions from vehicle metals by 2070 range from 4958 to 9218 megatonnes of carbon dioxide, with technological upgrading reducing emissions by 1051-1619 megatonnes. Incollaborative scenarios, demand management accounts for 64.3% of total emission reductions, while technologyoriented measures become increasingly important over the medium to long term. Overall, the findings demonstrate that unmanaged demand growth can substantially offset technological mitigation gains, highlighting the necessity of integrated demand- and technology-oriented strategies. This study provides a systemic and transferable framework to guide circular economy development and deep decarbonization transitions in vehicle fleets in China and other emerging economies.

Capacity planning for electric vehicle (EV) charging infrastructure has emerged as a critical challenge in developing low-carbon urban energy systems. This study proposes the first demand-driven, multi-objective planning model for optimizing city-scale capacity allocation of EV charging infrastructure. The model employs a bottom-up approach to estimate charging demand differentiated by vehicle type—battery electric vehicles (BEVs), extended-range electric vehicles (EREVs), and plug-in hybrid electric vehicles (PHEVs). Chongqing, a rapidly expanding EV industry cluster in China with a strong industrial base, supportive policies, and diverse urban morphologies, is selected as the case study. The results show that (1) monthly EV electricity consumption in Chongqing rose from 18.9 gigawatt-hours (GWh) in June 2022 to 57.5 GWh in December 2024, with associated carbon emissions increasing from 9.9 kilotons of carbon dioxide (ktCO2) to 30 ktCO2; (2) 181,622 additional charging piles were installed between 2022 and 2024, with the fastest growth observed in Yubei, reflecting a demand-responsive strategy that prioritizes areas with higher population density, higher income levels, and adequate land availability for pile deployment, rather than broad geographic coverage; and (3) between 2025 and 2030, EV electricity demand is projected to reach 1940 GWh, with the number of charging piles exceeding 1.4 million, and charging demand from EREVs and PHEVs expected to overtake BEVs later in the period. While Chongqing serves as the pilot area, the proposed planning platform is adaptable for application in cities worldwide, enabling cross-regional comparisons under diverse socio-economic, geographic, and policy conditions. Overall, this work offers policymakers a versatile tool to support sustainable, cost-effective EV infrastructure deployment aligned with low-carbon electrification targets in the transportation sector.

Although electric vehicles (EVs) are scaling rapidly, city-scale evidence on real-world operational energy use and carbon dioxide (CO2) emissions from EVs remains limited. Using Shanghai as a case study, this study develops a bottom-up framework covering all EV models registered between July 2022 and December 2024 to quantify model-specific real-world energy intensity, the operational energy mix, and associated CO2 emissions. The results indicate that (1) pronounced and systematic underestimation by test-cycle values: on average, real-world use is 20.8

Rapid electric vehicle (EV) expansion necessitates optimized charging infrastructure to bridge the persistent gaps between vehicle growth and charger availability. This study develops a demand-driven framework for city-scale EV charging demand assessment and charging pile capacity planning. It employs a bottom-up estimation approach to quantify electricity demand and a Harris Hawks Optimization algorithm to solve capacity planning challenges, capturing spatiotemporal demand variations across powertrain types and guiding allocation over 2022-2030 in Chongqing, China. The results show that (1) compared with June 2022, monthly EV electricity consumption tripled to 57.5 gigawatt-hours by the end of 2024, characterized by significant seasonal volatility and a structural shift in which the combined share of plug-in hybrid electric vehicles and extended-range electric vehicles reached 57.6

In the context of increasing global climate change, decarbonizing the residential building sector is crucial for sustainable development. This study aims to analyze the role of various influencing factors in carbon intensity changes using the decomposing structural decomposition (DSD) to assess and compare the potential and effectiveness of electrifying end-use activities during the operational phase of residential buildings worldwide for decarbonization. The results show that (1) while the electrification rate varied in its impact on emissions across different countries and regions, the overall increase in electrification contributed to higher carbon intensity. In contrast, changes in the emission factor of electricity generally made a positive contribution to emission reduction globally. (2) The global electrification level has significantly increased, with the electrification rate rising from 29.9 % in 2000 to 40.1 % in 2021. A 39.8 % increase in the electricity-related carbon emissions of global residential buildings was observed, increasing from 1452 MtCO2 to 2032 MtCO2, 2000-2021. (3) From 2000 to 2021, electrification of space heating was the main contributor to carbon reduction, whereas the contributions of electrification to cooling and lighting were relatively limited. Emission reductions from appliances and others remained stable. The electrification of water heating and cooking had varying effects on emission reductions in different countries. Furthermore, this study proposes a series of electrification decarbonization strategies. Overall, this study analyzes and contrasts decarbonization efforts from building electrification at the global and regional levels, explores the key motivations behind these efforts to aid national net-zero emission targets and accelerate the transition of the global residential building sector toward a carbon-neutral future.

Climate change and rising thermal comfort demand make residential heating and cooling central to building-sector decarbonization. This study presents the first bottom-up modeling framework to estimate residential heating and cooling loads across 30 Chinese provinces. The model, developed using EnergyPlus simulations of representative building prototypes, captures energy consumption patterns in both urban and rural housing over the period 1980-2024. The results indicate that: (1) In 2020, Guangdong recorded the highest cooling loads (76.5 TWh/a urban; 63.0 TWh/a rural). Henan exhibited the highest rural heating load (174.6 TWh/a), while urban heating loads were highest in Liaoning and Shandong. (2) Between 1980 and 2024, average urban cooling loads increased from 12.4 to 15.1 kWh/m2 a, whereas rural cooling loads declined from 22.63 to 19.87 kWh/m2 a. Urban heating loads decreased from 44.08 to 39.92 kWh/m2 a, and rural heating loads declined more markedly from 100.15 to 72.42 kWh/m2 a. (3) Urban residential floor area has exceeded rural stock in 22 provinces in recent years, compared with only four provinces in 2000. Moreover, the existence of 12 urban energy-efficiency standards versus a single rural standard highlights persistent envelope-performance disparities. These structural and regulatory differences have produced sustained urban-rural divergence in residential heating and cooling demand. The proposed framework provides a replicable basis for region-specific clean heating strategies and differentiated building standards to support carbon neutrality.

Renewable energy is believed to be one of the most low-carbon energy sources. Accurate forecasts of renewable energy generation can help the government make correct energy decisions. However, the sequence of renewable energy generation is irregular, nonlinear, and complex. And the existing techniques has problems such as being too linear or requiring a large amount of modeling data. Therefore, a new method is needed to solve these problems. A novel fractional grey model with Bessel function of the first kind as the grey input is proposed, and the discrete convolution solution is utilized to make the model viable in operation. The introduction of Bessel function of the first kind and fractional accumulation makes the model more adaptable and stable, and has better nonlinear fitting ability. The Salp Swarm Algorithm is used to determine the optimal nonlinear parameters of the proposed model. Compared with the existing 15 models in 5 developed countries in Europe and North America, the minimum forecasting MAPE of the proposed model just reaches 0.58

As a major contributor to global energy consumption and carbon emissions, the building sector plays a pivotal role in achieving carbon peaking and neutrality targets. This study systematically reviews the evolution of research on building stock energy conservation and emission reduction (BSECER) from 1992 to 2025, which is based on a comprehensive bibliometric analysis of 2643 publications. The analysis highlights the research contributions of countries, institutions, and scholars in the BSECER field, reveals patterns in collaborative networks, and identifies the development and shifting focus of research topics over time. The findings indicate that current BSECER research centers around four main areas: behavioral efficiency optimization, full life cycle carbon management, urban system transformation, and the integration of intelligent technologies, which collectively form a multiscale emission reduction framework from individual behavior to large-scale systems. Building on these insights, this study outlines five key future research directions: advancing comprehensive carbon neutrality technologies, accelerating the engineering application of intelligent technologies, developing innovative multi-scenario policy simulation tools, overcoming integration challenges in renewable energy systems, and establishing an interdisciplinary platform that links health, behavior, and energy conservation.

As an emerging emitter poised for significant growth in space cooling demand, India requires comprehensive insights into historical emission trends and decarbonization performance to shape future low-carbon cooling strategies. By integrating a bottom-up demand resource energy analysis model and a top-down decomposition method, this study is the first to conduct a state-level analysis of carbon emission trends and the corresponding decarbonization efforts for residential space cooling in urban and rural India from 2000 to 2022. The results indicate that (1) the carbon intensity of residential space cooling in India increased by 292.4 % from 2000 to 2022, reaching 513.8 kg of carbon dioxide per household. The net state domestic product per capita, representing income, emerged as the primary positive contributor. (2) The increase in carbon emissions from space cooling can be primarily attributed to the use of fans. While fan-based space cooling has nearly saturated Indian urban households, it is anticipated to persist as the primary cooling method in rural households for decades. (3) States with higher decarbonization potential are concentrated in two categories: those with high household income and substantial cooling appliance ownership and those with pronounced unmet cooling demand but low household income and hot climates. Furthermore, it is believed that promoting energy-efficient building designs can be prioritized to achieve affordable space cooling. Overall, this study serves as an effective foundation for formulating and promoting India's future cooling action plan, addressing the country's rising residential cooling demands and striving toward its net-zero goal by 2070.

Digital transformation, as a recent trend in socioeconomic development, is considered as a critical pathway for urban carbon reduction because of its potential to increase productivity and energy efficiency. However, few studies have explored the relationship between urban digitalization and carbon emissions (CE). Therefore, this study systematically analyzed the spatiotemporal distribution and interaction mechanism between digitalization and CE in the Yangtze River Delta (YRD) urban agglomerations of China during 2006-2020 based on a multidimensional indicator system, including digitalization industry level, digitalization application level, and urban green digitalization willingness. The findings revealed that both digitalization and CE in the YRD exhibit a significant and synchronously evolving "core-periphery" spatial pattern. Core cities generated substantial positive spillover effect on periphery cities through technology diffusion and policy demonstration, advancing both regional digitalization and the collaborative governance of CE. However, digitalization had dual impact on CE. On the one hand, it promoted the reduction of CE by enhancing energy efficiency, optimizing industrial structures, and promoting the application of green technologies. On the other hand, the expansion of digital infrastructure introduced a potential risk of increased energy consumption. Therefore, targeted policy recommendations are proposed to facilitate the coordination of environmental sustainability and digitalization in the YRD. This study provides empirical support and policy insights for advancing the coordinated development of regional digital transformation and green low-carbon initiatives.

Electrification is expected to accelerate the low-carbon transition in building operations globally. However, the extent to which building electrification reduces carbon emissions depends on the decarbonization of electricity. This study is the first to evaluate how the operational carbon intensity of commercial buildings has changed since 2000 and to assess the decarbonization impact of building electrification across different countries via the decomposing structural decomposition method. The key findings indicate that (1) the operational carbon intensity of commercial buildings showed a decreasing trend, with a sharper decline after 2011 (average annual decrease across 16 countries: -3.8 % per year). The electricity emission factor was a critical factor in mitigating carbon intensity growth. (2) Electrification rates hindered the decarbonization of commercial buildings, particularly for space heating (7.95 kgCO2/m2/year), which had the greatest negative impact. Other end-uses had smaller negative effects. However, after 2011, the impact of space heating weakened (2.06 kgCO2/m2/year), whereas the effects of appliances and space cooling began to reverse and contributed positively to decarbonization. (3) The increase in global electrification levels for commercial buildings led to limited decarbonization, with a total reduction of 2456 MtCO2 from 2001 to 2021. Furthermore, strategies to increase electricity decarbonization and accelerate the electrification of commercial buildings are proposed. In summary, this study evaluates the decarbonization impact of building electrification and the contributions of various end uses, offering valuable insights for governments to understand the true effects of building electrification and to develop effective decarbonization policies. had smaller negative effects. However, after 2011, the impact of space heating weakened (2.06 kgCO2/m2/year), whereas the effects of appliances and space cooling began to reverse and contributed positively to decarbonization. (3) The increase in global electrification levels for commercial buildings led to limited decarbonization, with a total reduction of 2456 MtCO2 from 2001 to 2021. Furthermore, strategies to increase electricity decarbonization and accelerate the electrification of commercial buildings are proposed. In summary, this study evaluates the decarbonization impact of building electrification and the contributions of various end uses, offering valuable insights for governments to understand the true effects of building electrification and to develop effective decarbonization policies.

Freight transport constitutes a critical yet hard-to-abate source of global carbon emissions. To facilitate sectoral transformation, this study develops a bottom-up framework integrating structural decomposition algorithm to investigate modal-specific emission patterns and transition potential across 35 national freight sectors from 2000 to 2022. Key findings reveal: (1) Rapid economic growth and freight demand drove a 917.8 MtCO2 increase in global freight emissions (mainly road freight) over two decades, despite gradually decelerating growth rates; (2) Structural modal shifts (e.g., rail replacing road freight) achieved cumulative decarbonization of 6.3 MtCO2, while mode-specific technological advances contributed 14 MtCO2; (3) Globally, while weak decoupling trajectories between emissions and freight demand prevailed, significant regional heterogeneity persists, underscoring untapped decarbonization potential in emerging economies. Overall, this work establishes a robust, data-driven framework for benchmarking freight decarbonization performance globally, offering actionable insights to align sectoral growth with climate goals and accelerate the transition to carbon-neutral freight systems.

Despite a substantial body of research-evidenced by our analysis of 2,628 peer-reviewed papers-global building floorspace data remain fragmented, inconsistent, and methodologically diverse. The lack of high-quality and openly accessible datasets poses major challenges to accurately assessing building carbon neutrality. This review focuses on global building floorspace, especially its nexus with energy and emissions. The key research areas include energy modeling, emissions analysis, building retrofits, and life cycle assessments. Each measurement approach-top-down, bottom-up, and hybrid-has its own limitations: top-down methods provide broad estimates but low accuracy, whereas bottom-up approaches are more precise but data intensive. Our simulations reveal a surge in floorspace growth across emerging economies-most notably in India, Indonesia, and Africa-with India's per capita floorspace projected to triple by 2070. We emphasize the need for a high-resolution global floorspace imagery database to compare energy efficiency, track decarbonization progress, and assess renovation impacts while promoting building sufficiency and accelerating the transition to net-zero building systems.

Natural gas production (NGP) and consumption (NGC) always exhibit high nonlinearity, posing challenges for accurate small-sample forecasting. In this work, a novel kernel ridge grey system model with an extended parametric Morlet wavelet (GMW-KRGM) is proposed by integrating the kernel ridge regularization and grey system modelling within a partially linear regression framework and trained by the conjugate gradient method to mitigate the ill-posed problem. Besides, a weighted multi-objective optimization strategy is designed for model hyperparameter optimization and solved by the grey wolf optimizer (GWO). Six real-world NGP and NGC forecasting cases are carried out and empirical results demonstrate that the proposed GMW-KRGM model with optimal hyperparameters solved by GWO always yields superior forecasting performance than the other 2 machine learning models and 7 conventional grey system benchmarks with out-of-sample mean average percentage error (MAPE) improved in 7.4245%–91.8392% and 14.7303%–42.67% on average, respectively and yields more precise forecasting accuracy with fast and stable convergence than the other 5 optimization algorithms with improved MAPE range from 9.5608% to 48.2584%, indicating that the proposed model holds the capability to effectively deal with the nonlinear complex system and has great potential in nonlinear small sample forecasting.

Assessing the emissions of plug-in hybrid electric vehicle (PHEV) operations is crucial for accelerating the carbon-neutral transition in the passenger car sector. This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China's top twenty selling PHEV models across different regions from 2020 to 2022. The results indicate that (1) the actual electricity intensity of the best-selling PHEV models (20.2-38.2 kWh/100 km) was 30-40 % higher than the New European Driving Cycle values, and the actual gasoline intensity (4.7-23.5 L/100 km) was 3-6 times greater than the New European Driving Cycle values. (2) The overall energy use of the best-selling models varied among different regions, and the energy use from 2020 to 2022 in Southern China was double that Northern China and the Yangtze River Middle Reach. (3) The top-selling models emitted 4.7 megatons of carbon dioxide nationwide from 2020 to 2022, with 1.9 megatons released by electricity consumption and 2.8 megatons released by gasoline combustion. Furthermore, targeted policy implications for expediting the carbon-neutral transition within the passenger car sector are proposed. In essence, this study explores and compares benchmark data at both the national and regional levels, along with performance metrics associated with PHEV operations. The main objective is to aid nationwide decarbonization efforts, focusing on carbon reduction and promoting the rapid transition of road transportation toward a net-zero carbon future.

Buildings produce one-third of carbon emissions globally; however, the absence of data regarding global floorspace poses challenges in advancing building carbon neutrality. We compile the measured building stocks for 14 major economies and apply our global building stock model (GLOBUS) to evaluate future trends in stock turnover. Based on a scenario not considering renovation, by 2070, the building stock in developed economies will be similar to 1.4 times that of 2020 (100 billion m(2)); in developing economies, it is expected to be 2.2 times that of 2020 (313 billion m(2)). Based on a techno-economic potential scenario, however, stocks in developed economies will decline to approximately 0.8 times the 2020 level, while stocks in developing economies will increase to nearly twice the 2020 level due to their having fewer buildings currently. Overall, GLOBUS provides a way to calculate the global building stock, helping scientists, engineers, and policymakers conduct a range of investigations across various future scenarios.

The building sector is the largest emitter globally and as such is at the forefront of the net-zero emissions pathway. This study is the first to present a bottom-up assessment framework integrated with the decomposing structural decomposition method to evaluate the emission patterns and decarbonization process of global residential building operations and commercial building operation simultaneously over the last two decades. The results reveal that (1) the average carbon intensity of global commercial building operations has maintained an annual decline of 1.94 industrial structures were generally the key to decarbonizing commercial building operations; (2) the operational carbon intensity of global residential buildings has maintained an annual decline of 1.2 and energy intensity and average household size have been key to this decarbonization; and (3) the total decarbonization of commercial building operations and residential buildings worldwide was 230.28 and 338.1 mega-tons of carbon dioxide per yr, respectively, with a decarbonization efficiency of 10.05 decarbonizing global building operations and closes the relevant gap, and it helps plan the stepwise carbon neutral pathway of future global buildings by the mid-century.