Search Results (179)

With the proposal of China’s “dual carbon” goal, the carbon market has become a vital tool for controlling carbon emissions. This study constructs a system dynamics model encompassing carbon trading, the economy, energy, population, and the environment, and conducts simulation analysis against the backdrop of China’s national carbon market’s implementation. The results indicate that the implementation of China’s national carbon market significantly promotes carbon emissions reduction, albeit at the cost of some economic development in the short term. However, the suppressive effect of the carbon market on carbon emissions is stronger than its negative impact on economic growth. The effects of carbon reduction strengthen with increases in carbon price, quota auction, CCER price, penalty severity, and the quota reduction rate and weaken with a higher CCER offset ratio. A moderate reduction in the tightening quota reduction rate is more conducive to achieving coordinated development across the multiple objectives of carbon reduction, economic development, and energy structure. Under the constraints of multiple objectives involving carbon reduction, economic development, and energy structure, the reasonable range for carbon prices is between CNY 77.9 and CNY 118.9 per ton, with the maximum quota auction of 23.4%. Additionally, the reasonable range for the quota reduction rates is between 0.84% and 2.18%, with the penalty severity set at 7. Full article

Forest carbon sink (FCS) is essential for achieving carbon neutrality and supporting sustainable development in ecologically fragile, resource-based cities such as those on the Loess Plateau. Despite the success of national afforestation programs, economic valuations of FCS at the city level remain limited. This study develops an integrated framework combining carbon stock estimation, regional carbon pricing, and net present value (NPV)-based valuation. Using Shenmu City in Shaanxi Province as a case study, forest carbon stocks from 2010 to 2023 are estimated based on the 2006 IPCC Guidelines. Future stocks (2024–2060) are projected using the GM (1,1) model. A dynamic pricing mechanism with a government-guaranteed floor price is applied under three offset scenarios (5%, 10%, 15%). The results show that Shenmu’s forest carbon stock could reach 20.67 million tonnes of CO₂ by 2060, and under a 15% offset scenario, the peak NPV reaches CNY 4.02 billion. Higher offset ratios increase FCS value by 18–22%, reflecting the growing scarcity of carbon credits. The pricing model improves market stability and investor confidence. This study provides a replicable approach for carbon sink valuation in semi-arid areas and offers policy insights aligned with SDG 13 (Climate Action) and SDG 15 (Life on Land). Full article

India’s revised nationally determined contribution at COP26 set a net-zero target for 2070, but the role of carbon dioxide removal (CDR) in achieving this goal remains unclear. This study quantifies the contribution of land-based CDR—bioenergy carbon capture and storage, biochar, and afforestation—in achieving India’s net-zero goal. Additionally, a stylised scenario explores an accelerated net-zero target by 2050 in India`s climate target. The global emission target is modelled to follow India’s climate ambition in both stylised scenarios. The results show that the ambitious 2050 net-zero pathway requires 56 GtCO₂ of cumulative novel CDR across the century, compared to 47 GtCO₂ under the 2070 scenario, with both requiring around 1 GtCO₂/year at net-zero. A higher ambitious pathway leads to increased economic costs, with a mid-century carbon price of USD 938, compared to USD 174 in the 2070 scenario. Without novel CDR methods, the cost of achieving net zero by 2050 quadruple. The accelerated 2050 net-zero pathway also intensifies land and water trade-offs, reducing land for crop production while increasing water demand for electricity and biomass. Despite these challenges, it limits end-of-century warming to 1.46 °C, compared to 1.79 °C under the 2070 scenario. These findings highlight the importance of clearly defined climate targets, scalable CDR strategies, and integrated resource management to balance climate ambition with sustainable development. Full article

Japan’s shift to a super-aged society is reshaping household carbon footprint (HCF) in ways that vary by age, income, and region. Drawing on a two-tier national–prefectural framework, we quantify the influence of demographic shifts on HCF and evaluate inequalities, and project prefectural HCF to 2050 under fixed 2005 technology and consumption baselines. Nationally, emissions follow an inverted-U age curve, peaking at the 50–54 s (2.16 tCO₂) and dropping at both the younger and older ends. Carbon inequality—the gap between high- and low-income households—displays the opposite U shape, being the widest below 30 and above 85. Regional HCF patterns add a further layer: while the inverted U persists, its peak shifts to the 60–64 s in high-income prefectures such as Tokyo—where senior emissions rise by 44% by 2050—and to the 45–49 s in low-income prefectures such as Akita, where younger age groups cut emissions by 58%. Although spatial carbon inequality narrows through midlife, it widens again in old age as eldercare and home energy needs grow. These findings suggest that a uniform mitigation trajectory overlooks key cohorts and regions. To meet the 2050 net-zero target, Japan should integrate age-, income-, and region-specific interventions—for example, targeted carbon pricing, green finance for middle-aged consumers, and less-urban low-carbon eldercare—into its decarbonization roadmap. Full article

Carbon markets play a pivotal role in achieving carbon peaking targets, with accurate price forecasting being essential for effective policymaking and corporate decision making. This study develops an integrated forecasting framework, combining an overall market model and a market-specific model, to predict carbon price trends in China from 2025 to 2026, while examining inter-market heterogeneity across eight regional markets. The overall market forecast reveals a fluctuating upward trend in the national carbon price over the next two years. Market-specific forecasts highlight significant disparities in price trends, as follows: the Shanghai and Guangzhou markets are projected to experience faster growth and the Beijing market to maintain stable prices, while the Tianjin and Chongqing markets exhibit more moderate increases. These disparities reflect the profound influence of regional economic levels, policy enforcement, and market maturity on carbon market development. By incorporating seasonal fluctuations and stochastic disturbances, we construct a forecasting model aligned with historical data dynamics and achieve differentiated forecasts through the analysis of historical price levels across markets, addressing the limitations of uniform target pricing in prior studies. These findings offer actionable insights for carbon market participants and policymakers, providing a robust foundation for designing differentiated carbon pricing policies to support China’s carbon peaking objectives. Full article

Guided by China’s “Dual Carbon” targets, the construction of its carbon market advances steadily. As a key policy mechanism for promoting emissions reduction and sustainable development, the emissions trading system plays a vital role in the national green transition strategy. Nonetheless, significant regional disparities exist in carbon emissions, and carbon allowance prices are subject to considerable fluctuations. This study examines the spatiotemporal evolution of China’s carbon emissions, investigating their distribution patterns across different regions. Furthermore, it analyzes the spatiotemporal changes in carbon allowance prices, focusing on their fluctuation patterns and spatial distribution, particularly regional differences in carbon market prices. This study focuses on the interplay between carbon emissions and carbon allowance prices, conducting an in-depth investigation into their interaction mechanisms. Using Shanghai as a case study, we construct a Vector Autoregression (VAR) model to empirically assess the dynamic impact of carbon emissions on carbon prices and their associated feedback effects. Subsequently, we propose policy recommendations for optimizing carbon market operations. This study enhances carbon markets’ functionality as climate governance tools, providing empirical and theoretical foundations for advancing low-carbon transitions and Sustainable Development Goals (SDGs). Full article

Background/Objectives: Addressing the global syndemic of obesity, undernutrition, and climate change requires a shift toward healthy and sustainable diets. This study examines the feasibility and cost implications of implementing a Healthy and Sustainable Basic Food Basket in Chile that aligns with the EAT–Lancet diet recommendations, through its comparison with the current Basic Food Basket used for the poverty line definition. Methods: The Healthy and Sustainable Basic Food Basket was constructed based on the EAT–Lancet dietary model and was uniquely adapted to reflect the observed consumption patterns of Chile’s lowest income quintile, allowing for a more realistic affordability assessment for vulnerable populations. Food prices from the National Institute of Statistics were analyzed over a 10-year period (2014–2023). Results: This study found that the Healthy and Sustainable Basic Food Basket provides 2001 kcal per day with a balanced macronutrient distribution. However, its average cost was 13.9% higher than the Basic Food Basket, posing a significant economic barrier for low-income populations. The cost gap varied seasonally, peaking in October (21.1% higher) and narrowing in December (4.6% higher). Long-term trends showed increasing costs for both baskets, with the Healthy and Sustainable Basic Food Basket reaching its highest price in 2023, further limiting affordability. Conclusions: These findings highlight the limitations of current poverty measurement frameworks in Chile, which prioritize caloric sufficiency over nutritional quality and sustainability. This suggests a need for policy revisions to incorporate the cost of healthy and sustainable diets into poverty assessments and social protection programs. Key policy recommendations include promoting healthier diets and improved food nutrition, supporting low-carbon foods, regulating local food production and supply systems, and encouraging seasonal, local consumption. This study underscores the need for structural interventions to ensure equitable access to sustainable diets, addressing both public health and environmental concerns. Full article

This study probes the complex interplay between tourism development (TDI), economic growth (GDP), and environmental sustainability, focusing on the ten most influential tourism nations: China, France, Italy, the United Kingdom, Mexico, Germany, Turkey, Spain, the United States, and Russia, covering the time from 1994 to 2023. This study uses feasible generalized least squares (FGLS) and Two-Stage Least Squares (2SLS) together with Driscoll–Kraay (DK) and panel quantile regression (PaQR) to examine the environmental as well as economic effects of TDI combined with trade openness (TOPE), foreign direct investment (FDI), energy prices (EPS), and population density (POPD). All models show that tourism development, indicated by TDI, and economic growth increase carbon emissions, demonstrating these variables’ adverse environmental impact. Energy prices, trade openness, and foreign direct investment lead to decreased emissions because these factors help promote energy-efficient clean technology. Furthermore, GDP growth positively influences TDI, while excessive carbon emissions negatively impact the appeal of tourism. The results indicate the need for sustainable tourism policies and investment in clean energy and green infrastructure, aligned with SDG 9, to foster innovation in energy-efficient practices and infrastructure. The research also supports SDG 13 by advocating climate-resilient tourism models and policies that decouple economic growth from environmental degradation. By adopting various advanced econometric approaches, this study provides strong evidence on the relationship between tourism, the macroeconomy, and environmental results. It offers fresh insights on how to achieve the growth of tourism and climate protection at the world’s top tourist destinations. Full article

Decarbonizing the transportation sector is critical for sustainable development, particularly in rapidly urbanizing countries like Turkiye. This study analyzes fuel demand elasticities for diesel, gasoline, and LPG across 12 NUTS-1 regions of Turkiye in 2022, using a panel random effects SUR approach. The model accounts for regional variation and fuel interactions, producing robust estimates that uncover significant spatial and temporal differences in consumption patterns. Uniquely, diesel demand displays a significantly positive price elasticity, challenging the conventional assumption of inelasticity. Gasoline demand is moderately price-sensitive, while LPG appears relatively unresponsive. Strong cross-price elasticities—especially between diesel and gasoline—point to substitution effects that can inform more adaptive policy frameworks. Seasonal fluctuations and Istanbul’s outsized impact also shape national trends. These findings underscore the need for differentiated region- and fuel-specific strategies. While higher gasoline taxes may effectively reduce demand, lowering diesel and LPG use will require complementary measures such as infrastructure upgrades, behavioral incentives, and accelerated adoption of alternative fuels. The study advocates for regionally adjusted carbon pricing, removal of implicit subsidies, and targeted support for electric and hybrid vehicles. Aligning fiscal tools with actual demand behavior can enhance both the efficiency and equity of the transition to a low-carbon transportation system. Full article

This study analyzes the economic impacts of integrating floating offshore wind farms with a Floating Production, Storage and Offloading (FPSO) unit to reduce carbon dioxide emissions. The idea is to replace the use of natural gas for power supply with an offshore wind farm, considering the effects of carbon pricing. Results show that wind integration reduces emissions by 23% to 76%, depending on the installed capacity. However, higher wind capacity increases total system costs, initial investment, electricity and operational expenses. The Brazilian carbon credit market adversely impacts existing FPSO units as a result of the compulsory carbon trading costs necessary to mitigate their emissions. In contrast, wind-integrated scenarios benefited from carbon pricing, improving financial indicators such as payback period and Return on Investment. Wind shares of 30% and 70% yielded the best financial results for carbon prices between 10 and 50 United States Dollars per ton, with higher penalties further improving viability. These findings elucidate the significance of carbon pricing in mitigating emissions and enhancing the economic feasibility of offshore wind farms within the context of the Brazilian national FPSO decarbonization strategy. Full article

In the context of the clean and low-carbon transformation of power systems, addressing the challenge of day-ahead electricity market price prediction issues triggered by the strong stochastic volatility of power supply output due to high-penetration renewable energy integration, as well as problems such as limited dataset scales and short market cycles in test sets associated with existing electricity price prediction methods, this paper introduced an innovative prediction approach based on a multi-modal feature fusion and BiGRUSA-ResSE-KAN deep learning model. In the data preprocessing stage, maximum–minimum normalization techniques are employed to process raw electricity price data and exogenous variable data; the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD) methods are utilized for multi-modal decomposition of electricity price data to construct a multi-scale electricity price component matrix; and a sliding window mechanism is applied to segment time-series data, forming a three-dimensional input structure for the model. In the feature extraction and prediction stage, the BiGRUSA-ResSE-KAN multi-branch integrated network leverages the synergistic effects of gated recurrent units combined with residual structures and attention mechanisms to achieve deep feature fusion of multi-source heterogeneous data and model complex nonlinear relationships, while further exploring complex coupling patterns in electricity price fluctuations through the knowledge-adaptive network (KAN) module, ultimately outputting 24 h day-ahead electricity price predictions. Finally, verification experiments conducted using test sets spanning two years from five major electricity markets demonstrate that the introduced method effectively enhances the accuracy of day-ahead electricity price prediction, exhibits good applicability across different national electricity markets, and provides robust support for electricity market decision making. Full article

Based on the economic and climate policy uncertainty index and the price data of major carbon emission trading markets from May 2014 to August 2023, this paper uses the generalized autoregressive conditional heteroskedasticity and mixing data sampling (GARCH-MIDAS) model to analyze the impact of policy uncertainty on carbon market price volatility. The results indicate the following: (1) The price volatility in the Hubei carbon market is influenced by both economic and climate policy uncertainties, while the Guangdong market is only affected by climate policy uncertainty, and the Shenzhen carbon market is only affected by economic policy uncertainty. (2) Before the establishment of the national carbon market, the carbon market prices in Hubei were impacted by both policy uncertainties, while Guangdong and Shenzhen carbon markets were only affected by climate policy uncertainties. (3) On the contrary, after the establishment of the national carbon market, only the Shenzhen carbon market was affected by both policy uncertainties, and the price volatility in the Guangdong and Hubei carbon markets was not affected by policy uncertainties. The above research conclusions are helpful for regulatory agencies and policymakers to assess the future direction of the pilot carbon market and provide an empirical basis for preventing and resolving policy risks. At the same time, the proposed GARCH-MIDAS model effectively solves the inconsistent frequency problem of policy uncertainty and carbon price volatility, providing a new perspective for the study of factors affecting carbon market volatility. Full article

Brazil, as an emerging and newly industrialized nation, presents a complex dynamic between economic advancement and environmental sustainability. This study investigates the influence of coal consumption (COAL), gross domestic product (GDP), renewable energy (REN), and foreign direct investment (FDI) on CO₂ emissions in Brazil using quarterly data from 1990Q1 to 2020Q4. Employing the Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) method and the Quantile-on-Quantile Granger Causality (QQGC) test, we uncover significant nonlinear and distributional heterogeneities in these relationships. Results show that COAL, GDP, and FDI consistently exert a positive impact on CO₂ emissions across most quantiles, whereas REN significantly reduces emissions, particularly at the upper emission quantiles. Causality analysis confirms that all four variables are significant predictors of CO₂ emissions. The study contributes methodologically by applying QQKRLS and QQGC to reveal nuanced interactions across the emissions distribution—an advancement over traditional linear approaches. Empirically, it provides Brazil-specific evidence of the dual role of FDI and economic growth in both driving emissions and offering potential for sustainable transition. Based on these findings, we recommend policies that prioritize sector-specific FDI screening to promote green technologies, accelerate investment in renewable energy infrastructure, and impose adaptive carbon pricing mechanisms that reflect the heterogeneous impact of coal and economic growth on emissions. These insights support Brazil’s climate targets and guide a balanced path toward inclusive and sustainable development. Full article

The Paris Agreement is aimed at keeping global warming well below 2 °C while pursuing efforts to limit it below 1.5 °C; however, achieving these goals implies a tight limit on cumulative net carbon emissions, which includes CO₂, CH₄, and NO₂. Moreover, the focus of carbon emission policies should differ from country to country depending on their national circumstances. In this study, based on forecast models, specifically, in 2005, the average annual per-capita CO₂ emissions was recorded as 6.8 tons for Brazil, 4.8 tons for China, 8.4 tons for EU28, 1.2 tons for India, 10.1 tons for Japan, 9.0 tons for Russia, and 18.6 tons for the USA. The carbon intensity is expected to range from 37% to 85% across the studied regions. Based on the AIM, POLES, and IMAGE models, the projected carbon prices for 2050 are estimated at USD 2000, USD 2045, and USD 940 per ton of CO₂, measured in 2005 US dollars, respectively. The forecast data support carbon policy making in major countries. Full article

This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development. Full article