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As the world’s largest energy consumer and carbon emitter, the task of carbon emission reduction is imminent. In order to realize the dual-carbon goal at an early date, it is necessary to study the key factors affecting China’s carbon emissions and their non-linear relationships. This paper compares the performance of six machine learning algorithms to that of traditional econometric models in predicting carbon emissions in China from 2011 to 2020 using panel data from 254 cities in China. Specifically, it analyzes the comparative importance of domestic economic, external economic, and policy uncertainty factors as well as the nonparametric relationship between these factors and carbon emissions based on the Extra-trees model. Results show that energy consumption (ENC) remains the root cause of increased carbon emissions among domestic economic factors, although government intervention (GOV) and digital finance (DIG) can significantly reduce it. Next, among the external economic and policy uncertainty factors, foreign direct investment (FDI) and economic policy uncertainty (EPU) are important factors influencing carbon emissions, and the partial dependence plots (PDPs) confirm the pollution haven hypothesis and also reveal the role of EPU in reducing carbon emissions. The heterogeneity of factors affecting carbon emissions is also analyzed under different city sizes, and it is found that ENC is a common driving factor in cities of different sizes, but there are some differences. Finally, appropriate policy recommendations are proposed by us to help China move rapidly towards a green and sustainable development path.

Global climate change associated with increased carbon emissions has become a global concern. Resource-based cities, by estimations, have emerged as major contributors to carbon emissions, accounting for approximately one-third of the national total. This underscores their pivotal role in the pursuit of carbon neutrality goals. Despite this, resource-based cities have long been neglected in current climate change mitigation policy discussions. Accordingly, using exploratory spatial data analysis and Geographical Weighted Regression method, this study investigates the determinants of carbon emissions and their spatial pattern in 113 resource-based cities in China. It can be concluded that: (1) The proportion of carbon emissions from resource-based cities in the national total has shown a marginal increase between 2003 and 2017, and the emissions from these cities have not yet reached their peak. (2) A relatively stable spatial pattern of “northeast high, southwest low” characterizes carbon emissions in resource-based cities, displaying significant spatial autocorrelation. (3) Population size, economic development level, carbon abatement technology, and the proportion of resource-based industries all contribute to the increase in carbon emissions in these cities, with carbon abatement technology playing a predominant role. (4) There is a spatial variation in the strength of the effects of the various influences.

Most people around the world have felt the effects of climate change on their quality of life. This study sought to achieve the maximum efficiency for climate change actions with the minimum negative impact on the well-being of countries and cities. The Climate Change and Country Success (C 3 S) and Climate Change and Cities’ Quality of Life (C 3 QL) models and maps of the world created as part of this research showed that as economic, social, political, cultural, and environmental metrics of countries and cities improve, so do their climate change indicators. For the 14 climate change indicators, the C 3 S and C 3 QL models indicated 68.8% average dispersion dimensions in the case of countries and 52.8% in the case of cities. Our research showed that increases in the success of 169 countries saw improvements in 9 climate change indicators out of the 12 considered. Improvements in country success indicators were accompanied by a 71% improvement in climate change metrics.

Carbon pricing can steer energy choices towards low-carbon fuels and foster energy conservation efforts. Simultaneously, higher fossil fuel prices may exacerbate energy poverty. A just portfolio of climate policies therefore requires a balanced instrument mix to jointly combat climate change and energy poverty. We review recent policy developments in the EU aimed at addressing energy poverty and the social implications of the climate neutrality transition. We then operationalise an affordability-based definition of energy poverty and numerically illustrate that recent EU climate policy proposals risk raising the number of energy poor when not accompanied with complementary measures, while alternative climate policy designs could lift more than 1 million households out of energy poverty through income-targeted revenue recycling schemes. While these schemes have low informational requirements and appear sufficient to avoid exacerbating energy poverty, the findings suggest that more tailored interventions are needed. Finally, we discuss how insights from behavioural economics and energy justice can help shape optimal policy packages and processes.

A high-quality grain distribution system is the key to guarantee the balance of grain supply and demand, and the reduction of greenhouse gas emissions in grain transportation is the concern of the government and enterprises. In order to clarify the influence of different low-carbon policies and loading modes on the optimization of grain multimodal transport paths, this paper constructs a low-carbon grain multimodal transport path optimization model with the objective of minimizing transportation, cargo loss, time and carbon emission costs. Taking Jiamusi City, the main grain producing area in China, as an example, a heuristic genetic algorithm is used to solve the model to explore the impacts of carbon tax policy (CTP), carbon emission trading scheme (ETS) policy and carbon offset policy (COP) on the transportation schemes of grain in three loading modes, namely, “bagged, bulk and containerized”. We analyze the effects of carbon price fluctuations on decision-making under low carbon quota, medium carbon quota and high carbon quota scenarios, and study the effects of different cost preference values on transportation decision-making under the ETS policy. Under the ETS policy, the optimal transportation path of each loading mode has the lowest total cost, and the total cost is reduced by 1% compared with that of carbon tax and carbon offset policy. Among them, the containerized rail-water intermodal transportation scheme has obvious cost and environmental advantages, with the total cost decreasing by 42% and 33% compared to bag and bulk, and the carbon emission decreasing by 27% compared to both. With the overall relaxation of the time window, the transportation scheme is transformed from road-rail intermodal transportation to rail-water intermodal transportation. In addition, when the carbon price is RMB 2/kgCO and above, it can promote the transportation transition to low-carbon rail-water intermodal transportation, and the high carbon quota under the ETS policy can motivate enterprises to realize cost reduction and efficiency. The findings of the study can provide reference for grain transportation enterprises to formulate multimodal transportation solutions and provide theoretical support for the government to formulate low-carbon policies.

While transitioning from polluting cooking fuels (e.g. wood, charcoal) to cleaner fuels, like liquefied petroleum gas (LPG), can lead to time savings, the amount of time saved is uncertain due to minimal stove use monitoring (SUM) data. Approximately three months (mean:82 days (SD:41)) of SUM data from Geocene temperature sensors was collected from 186 households in Mbalmayo, Cameroon; Obuasi, Ghana and Eldoret, Kenya. Households exclusively using LPG (mean:1 h 22 min/day) cooked for two hours/day less than those stacking LPG and polluting fuels (3 h 19 min/day), and almost three hours/day less than those exclusively using polluting fuels (4 h 10 min/day). Financially insecure households exclusively using polluting fuels cooked for ~ 45 min longer (4 h 29 min) than financially secure households (3 h 45 min). During a 24-hour household air pollution (HAP) monitoring period, average cooking time was 38 min longer (3 h 48 min vs. 3 h 10 min) and households cooked nearly once more per day (3.63 events) than during the remaining SUM period (2.72 events). Longer cooking times among financially insecure polluting fuel users suggests that LPG access may disproportionately benefit poorer households via greater time savings. Households may cook for longer-than-normal when monitored for HAP.

The impact of climate change on migration has gained both academic and public interest in recent years. Here we employ a meta-analysis approach to synthesize the evidence from 30 country-level studies that estimate the effect of slow- and rapid-onset events on migration worldwide. Most studies find that environmental hazards affect migration, although with contextual variation. Migration is primarily internal or to low- and middle-income countries. The strongest relationship is found in studies with a large share of countries outside the Organisation for Economic Co-operation and Development, particularly from Latin America and the Caribbean and sub-Saharan Africa, and in studies of middle-income and agriculturally dependent countries. Income and conflict moderate and partly explain the relationship between environmental change and migration. Combining our estimates for differential migration responses with the observed environmental change in these countries in recent decades illustrates how the meta-analytic results can provide useful insights for the identification of potential hotspots of environmental migration.Using a meta-analysis approach, the authors find robust evidence that environmental factors play a role in explaining migration patterns across countries and over time, but the size of the effects depend on the economic and sociopolitical context, and the environmental factors considered.

This study examines the carbon emissions of star-rated hotels in Hangzhou, comparing the environmental impact of prefabricated construction (PC) and conventional construction (CC) methodologies. The research reveals that PC generally results in lower carbon emissions during the materialization phase, with notable variations across different hotel star levels and administrative regions. Higher-star hotels exhibit higher total emissions, primarily due to larger scale and reliance on conventional construction methods. In contrast, lower-tier hotels benefit more consistently from the adoption of prefabricated construction, leading to significant reductions in carbon emissions. Regional analysis shows that the impact of the COVID-19 pandemic on hotel turnover and carbon decoupling trends varies, with core urban areas experiencing a more pronounced decoupling effect, while suburban regions exhibited slower recovery. The findings underscore the potential for prefabricated construction to reduce carbon footprints, particularly in mid-tier and lower-tier hotels. This study contributes to the understanding of sustainable construction practices in the hotel industry and provides a foundation for future research focused on refining carbon emission assessments, incorporating real-world data, and exploring the integration of renewable energy and lifecycle emissions.

This study investigates how cattle production, energy intensity in agriculture, and environmental degradation (cattle-related CO 2eq emissions) interact in Latin America and the Caribbean (LAC). Using Vector Autoregressive (VAR) and Panel VAR models on data from 12 countries between 2000 and 2018, the results show that cattle production Granger-causes both energy intensity and CO 2eq emissions, and that energy intensity itself Granger-causes CO 2eq emissions, indicating a clear, unidirectional causal chain. Impulse-response functions suggest that shocks to cattle production initially raise energy intensity and CO 2eq emissions but eventually lead to reduced CO 2eq emissions (at the end of the study period). Forecasts project rising cattle output, declining energy intensity, and a gradual decrease in CO 2eq emissions, suggesting partial decoupling of productivity from environmental harm. Although energy efficiency gains offer mitigation potential, sustained growth in cattle production may offset these improvements without systemic change. These findings underscore the critical role of clean energy and sustainable intensification in cattle systems. The framework developed is applicable beyond LAC, offering insights for other regions facing similar challenges in balancing agricultural growth with environmental sustainability.

Growing public concern over environmental issues has heightened the significance of examining the impact of informal environmental regulation (IER) on total-factor carbon emission performance (TFCEP), offering a novel pathway to advance sustainable development goals (SDGs). This study establishes a theoretical framework elucidating the influence of IER on TFCEP, followed by empirical analysis utilizing China’s provincial panel data from 2004 to 2019 through fixed-effects models, threshold regression, and the spatial Durbin model. The results demonstrate that: (1) IER exerts a significant positive influence on TFCEP. This significant impact persists even after rigorous endogeneity and robustness checks are conducted. Moreover, this impact is particularly pronounced during the 2012–2019 period and in western regions. (2) While the short-term impact of IER on TFCEP is statistically insignificant, it exhibits a significant positive effect once the intensity of IER or the level of green technology innovation surpasses specific thresholds. (3) IER not only improves local TFCEP but also generates substantial spatial spillover effects, elevating TFCEP in neighboring regions. By investigating the low-carbon transition effects of public participation in environmental governance, this study provides policymakers with fresh theoretical insights and empirical evidence to support SDGs.