Explore the vast range of titles available.

With the continuous advancement of technology, carbon emission prediction results have become increasingly reliable. However, traditional carbon emission prediction methods face limitations in data and uncertainty, requiring substantial experimental resources and data. Therefore, the study optimizes the Support Vector Machine through the Improved Grey Wolf Optimizer. It combines the extended stochastic environmental impact assessment model to provide a framework for influencing factors and introduces randomness and regression analysis. This approach improves the accuracy and applicability of the fusion model in predicting carbon emissions in industrial zones. Experimental results show that, in the Hubei Province dataset, the proposed model achieves the smallest Mean Squared Error of 0.0075 among four models. The Root Mean Squared Error values of the individual Feedforward Neural Network and Multilayer Perceptron are 0.0101 and 0.0197 higher than that of the proposed model, respectively. Compared to existing single models, such as backpropagation neural networks, the Root Mean Squared Error values of the studied model is significantly reduced by 12%. These results indicate that the proposed prediction model demonstrates excellent timeliness in carbon emission forecasting. This capability provides policy makers with a variety of policy assessment tools to help develop more effective emissions reduction policies.

Climate change (CC) and air pollution are closely interlinked environmental challenges that significantly affect human health and quality of life, especially in urban and industrialized regions. This study conducted a comprehensive investigation on how future climate scenarios may affect air quality and related human impacts, using a Southern European country (Portugal) for illustration. The study employed the most up-to-date future climate projections (Shared Socioeconomic Pathways—SSP) that were dynamically downscaled for Portugal. High-resolution simulations were carried out using the Weather Research & Forecasting (WRF) model, providing data for relevant meteorological variables that most affect air quality, for three future climate scenarios: fossil-fueled development (SSP5-8.5), regional inequality (SSP3-7.0), and a middle-of-the-road future (SSP2-4.5). Current and future air quality was simulated with the CHIMERE chemical transport model driven by WRF downscaled data and future emissions from the SSP v2.0 database. Results show that CC will impact nitrogen oxides (NO[sub.2]), ozone (O[sub.3]), and particulate matter (PM) concentrations over Portugal, with only agricultural emissions increasing in all scenarios. PM and NO[sub.2] will decrease in urban areas, over the short and long term, mainly for more conservative scenarios (SSP2-4.5 and SSP3-7.0), while O[sub.3] will increase over mainland Portugal (except for coastal/urban areas). Regarding human health, premature deaths are expected to be highest in urban areas, with reductions projected for NO[sub.2] and PM[sub.2.5] under SSP2-4.5 and increases in O[sub.3]-related mortality under SSP5-8.5. Overall, SSP2-4.5 presents the most sustainable outcomes, highlighting the importance of integrating air quality management and health impact assessments into climate adaptation strategies to promote long-term environmental sustainability in southern Europe, consistent with the United Nations Sustainable Development Goals (SDGs).

In 2020, China pledged carbon reduction targets at the United Nations: peaking emissions by 2030 and achieving carbon neutrality by 2060. Research and prediction of regional carbon emissions are crucial for achieving these dual carbon targets across China. This study aims to construct an indicator system for regional carbon emissions and utilize it for forecasting. Analyzing carbon emission data from a specific area in Hainan Province from 2010 to 2020, we established an indicator system. Using the interpretable SHAP model, we assessed indicator importance and trends. Employing an improved STIRPAT model with partial least squares regression to address multicollinearity among influencing factors, we developed a carbon emission prediction model. Based on this, we forecasted carbon emissions from 2021 to 2060 in the specified area under three scenarios: natural, baseline, and ambitious. The results show that the growth of resident population and per capita GDP has the most significant promoting effect on carbon emissions in the region while optimizing industrial structure, energy consumption structure, and reducing energy intensity will inhibit carbon emissions. The prediction results indicate that in the natural scenario, regional carbon emissions will peak in 2035, and achieving carbon neutrality by 2060 is not feasible, while the baseline scenario and ambitious scenario can achieve the dual carbon targets on time or even earlier. The research results of this article provide a reference method for predicting carbon emissions in other regions and a guide for future regional emission reduction.

With the increasing prominence of the global carbon emission problem, the accurate prediction of carbon emissions has become an increasingly urgent need. Existing carbon emission prediction methods have the problems of slow calculation speed, inaccurate prediction, and insufficient deep mining of influencing factors when dealing with large-scale data. In this study, a comprehensive carbon emission prediction method is proposed. Firstly, multiple influencing factors including economic factors and demographic factors are considered, and a pathway analysis method is introduced to mine the long-term relationship between these factors and carbon emissions. Then, indirect influence terms are added to the multiple regression equation, and the variable is used to represent the indirect influence relationship. Finally, this study proposes the PCA-PA-MBGD method, which applies the results of principal component analysis to the pathway analysis. By reducing the data dimensions and extracting the main influencing factors, and optimizing the carbon emission prediction model by using a mini-batch stochastic gradient descent algorithm, the results show that this method can process a large amount of data quickly and efficiently, and realize an accurate prediction of carbon emissions. This provides strong support for solving the carbon emission problem and offers new ideas and methods for future related research.

Against the backdrop of increasing extreme weather events associated with global climate change, regulating carbon dioxide emissions, a primary contributor to atmospheric warming, has emerged as a pressing global challenge. Focusing on China as a representative case study of major developing economies, this research examines industrial carbon emission patterns during 2001–2022. Methodologically, it introduces an innovative analytical framework that integrates the Generalized Divisia Index Method (GDIM) with the Low Emissions Analysis Platform (LEAP) to both decompose industrial emission drivers and project future trajectories through 2040. Key findings reveal that:the following: (1) Carbon intensity in China’s industrial sector has been substantially decreasing under green technological advancements and policy interventions. (2) Industrial restructuring demonstrates constraining effects on carbon output, while productivity gains show untapped potential for emission abatement. Notably, the dual mechanisms of enhanced energy efficiency and cleaner energy transitions emerge as pivotal mitigation levers. (3) Scenario analyses indicate that coordinated policies addressing energy mix optimization, efficiency gains, and economic restructuring could facilitate achieving industrial carbon peaking before 2030. These results offer substantive insights for designing phased decarbonization roadmaps, while contributing empirical evidence to international climate policy discourse. The integrated methodology also presents a transferable analytical paradigm for emission studies in other industrializing economies.

Reducing greenhouse gas (GHG) emissions is a global concern after Paris Agreement (PA). Identification of GHG emission sources and accurate and precise estimation of the corresponding emissions is the first step to meet reduction targets under PA. Increasing share of agricultural emissions in the global concentration has raised concerns on this sector. Now, reducing agricultural emissions without compromising food security is a real challenge. The present study was aimed to provide the current emission profile of Pakistan’s agriculture, historical emission trends and future projections under agricultural growth scenarios according to prescribed guidelines of Intergovernmental Panel on Climate Change (IPCC) for national GHGs inventory development. In this study, GHG emissions were estimated using United Nations Framework Convention on Climate Change (UNFCCC) Non-Annex-I Inventory Software (NAIIS), version 1.3.2 as per prescribed Revised 1996 IPCC Guidelines. In these emission estimations, tier-1 approach (which employs default emission factors) was used in accordance with national circumstances and data availability in the country. The emissions baseline was projected for 2030 under business as usual (BAU), food security (FS) and enhanced consumption pattern (ECP) scenarios. Agriculture sector emitted 174.6 million tons (Mt) of carbon dioxide equivalent (CO 2 -equivalent) emissions, of which 89.8 Mt is methane (CH 4 ) and 83.7 Mt is nitrous oxide (N 2 O). Carbon monoxide (CO) emissions were found to be 1.07 Mt of CO 2 -equivalent. Emission from agricultural soils constituted 45.5% of the total agricultural emissions followed by 45.1% from enteric fermentation and 6.5% from livestock manure management. The rest of 1.7% of the emissions were from rice cultivation followed by 1.1% from burning of crop residue. Historical emission trends showed that the agricultural emissions grew from 71.6 to 174.6 Mt of CO 2 -equivalent from 1994 to 2015, a 143.8% increase over the period of 21 years. Emissions baseline projections were found to be 271.9, 314.3 and 362.9 Mt tons of CO 2 -equivalent under BAU, FS and ECP scenarios, respectively.

In the global sustainable development, China has taken the lead in proposing the dual-carbon goals of carbon neutrality and carbon peaking, and the problem lies in how to find the key influencing factors and construct the future development path based on the influencing factors. This paper takes the southeast coastal region of China as the scope from 2010 to 2020, and first establishes the Kuznets model and the factor decomposition model to analyse the relationship between regional carbon emissions and economic and energy consumption, respectively; then chooses the BP-LSTM model as the basis of predicting future regional carbon emissions; and finally sets up a regional dual-carbon target using scenario analysis to solve the path planning method. The study shows that the relationship between economic development and regional carbon emissions is inverted ‘U’-shaped, and that industrial upgrading and energy decarbonisation in the dual-carbon planning will be conducive to future emission reduction and carbon abatement.

The sensitivity of air quality model responses to modifications in input data (e.g. emissions, meteorology and boundary conditions) or model configurations is recognized as an important issue for air quality modelling applications in support of air quality plans. In the framework of FAIRMODE (Forum of Air Quality Modelling in Europe, https://fairmode.jrc.ec.europa.eu/) a dedicated air quality modelling exercise has been designed to address this issue. The main goal was to evaluate the magnitude and variability of air quality model responses when studying emission scenarios/projections by assessing the changes of model output in response to emission changes. This work is based on several air quality models that are used to support model users and developers, and, consequently, policy makers. We present the FAIRMODE exercise and the participating models, and provide an analysis of the variability of O3 and PM concentrations due to emission reduction scenarios. The key novel feature, in comparison with other exercises, is that emission reduction strategies in the present work are applied and evaluated at urban scale over a large number of cities using new indicators such as the absolute potential, the relative potential and the absolute potency. The results show that there is a larger variability of concentration changes between models, when the emission reduction scenarios are applied, than for their respective baseline absolute concentrations. For ozone, the variability between models of absolute baseline concentrations is below 10%, while the variability of concentration changes (when emissions are similarly perturbed) exceeds, in some instances 100% or higher during episodes. Combined emission reductions are usually more efficient than the sum of single precursor emission reductions both for O3 and PM. In particular for ozone, model responses, in terms of linearity and additivity, show a clear impact of non-linear chemistry processes. This analysis gives an insight into the impact of model’ sensitivity to emission reductions that may be considered when designing air quality plans and paves the way of more in-depth analysis to disentangle the role of emissions from model formulation for present and future air quality assessments.

The vehicle fleets in Latin America are increasingly incorporating hybrid electric vehicles due to the economic and non-economic incentives provided by governments aiming to reduce energy consumption and emissions in the transportation sector. However, the impacts of implementing hybrid vehicles remain uncertain, especially in Latin American, which poses a risk to the achievement of environmental objectives in developing countries. The aim of this study is to evaluate the benefits of incorporating hybrid vehicles to replace internal combustion vehicles, considering the improvement in the level of emission standards. This study uses data reported by Colombian vehicle importers during the homologation process in Colombia and the number of vehicles registered in the country between 2010 and 2022. The Gompertz model and logistic growth curves are used to project the total number of vehicles, taking into account the level of hybridization and including conventional natural gas and electric vehicles. In this way, tailpipe emissions and energy efficiency up to 2040 are also projected for different hybrid vehicle penetration scenarios. Results show that the scenario in which the share of hybrid vehicles remains stable (Scenario 1) shows a slight increase in energy consumption compared to the baseline scenario, about 1.72% in 2035 and 2.87% in 2040. The scenario where the share of MHEVs, HEVs, and PHEVs reaches approximately 50% of the vehicle fleet in 2040 (Scenario 2) shows a reduction in energy consumption of 24.64% in 2035 and 33.81% in 2040. Finally, the scenario that accelerates the growth of HEVs and PHEVs while keeping MHEVs at the same level of participation from 2025 (Scenario 3) does not differ from Scenario 2. Results show that the introduction of full hybrids and plug-in hybrid vehicles improve fleet fuel consumption and emissions. Additionally, when the adoption rates of these technologies are relatively low, the benefits may be questionable, but when the market share of hybrid vehicles is high, energy consumption and emissions are significantly reduced. Nevertheless, this study also shows that Mild Hybrid Electric Vehicles (MHEVs) do not provide a significant improvement in terms of fuel consumption and emissions.

Supported by the coordinated development strategy, the Beijing–Tianjin–Hebei (BTH) region has achieved rapid development but also faces severe energy consumption and environmental pollution problems. As the main responsibility of emission reduction, the coordinated and orderly implementation of carbon emission reduction in Beijing, Tianjin, and Hebei is of great significance to the realization of the carbon neutrality target. Based on this, this study comprehensively uses the expanded STIRPAT model, optimized extreme learning machine (ELM) network, entropy method, and zero-sum gains DEA (ZSG-DEA) model to explore the carbon emission drivers, long-term emission reduction pathway, and carbon quota allocation in the BTH region. The results of the driving factor analysis indicate that the proportion of non-fossil energy consumption is a significant driving factor for Beijing’s carbon emissions, and the improvement of the electrification level can inhibit the carbon emissions. The total energy consumption has the greatest impact on the carbon emissions of Tianjin and Hebei. The simulation results reveal that under the constraint of the carbon neutrality target, Beijing, Tianjin, and Hebei should formulate more stringent emission reduction measures to ensure that the overall carbon emission will reach its peak in 2030. The cumulative emission reduction rate should exceed 60% in 2060, and negative carbon technology should be used to offset carbon emissions of not less than 360 million tons (Mt) per year by 2060. Furthermore, the allocation results show that Beijing will receive a greater carbon quota than Hebei. The final allocation scheme will greatly promote and encourage carbon emission reduction in Hebei Province, which is conducive to achieving the goal of carbon neutrality.