Search Results (142)

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

Accurate electricity wholesale price (EWP) forecasting is crucial for advancing sustainability in the energy sector, as it supports more efficient utilization and integration of renewable energy by informing when and how it should be consumed, dispatched, curtailed, or stored. However, high fluctuations in EWP, often resulting from demand–supply imbalances typically caused by sudden surges in electricity usage and the intermittency of renewable energy generation, and unforeseen external events, pose a challenge for accurate forecasting. Incorporating local temporal information (LTI) in time series, such as hourly price changes, is essential for accurate EWP forecasting, as it helps detect rapid market shifts. However, existing methods remain limited in capturing LTI, either relying on point-wise input sequences or, for fixed-length, non-overlapping segmentation methods, failing to effectively model dependencies within and across segments. This paper proposes the Local-Temporal Convolutional Transformer (LT-Conformer) model for day-ahead EWP forecasting, which addresses the challenge of capturing fine-grained LTI using Local-Temporal 1D Convolution and incorporates two attention modules to capture global temporal dependencies (e.g., daily price trends) and cross-feature dependencies (e.g., solar output influencing price). An initial evaluation in the Australian market demonstrates that LT-Conformer outperforms existing state-of-the-art methods and exhibits adaptability in forecasting EWP under volatile market conditions. Full article

This study reviews life cycle assessments (LCAs) of reprocessed single-use devices (rSUDs) in healthcare to quantify their greenhouse gas (GHG) emission reductions compared to original equipment manufacturer (OEM) SUDs (single-use devices). rSUDs offer notable reductions in solid waste generation, but, until recently, a reduction in greenhouse gases and other emissions from the reprocessing process was only hypothesized. Emerging LCAs in this space can help validate the assumptions of better environmental performance from greater circularity in the medical device industry. Four LCAs analyzing eight devices found consistent and significant GHG reductions ranging from 23% to 60% with rSUD use. Primary data from rSUD manufacturers were utilized in all studies, with SimaPro v9.3.0.2 and Ecoinvent v3.8 being the predominant LCA software and database. Raw material extraction and production dominated SUD emissions, while electricity use and packaging materials were key contributors for rSUDs. Sensitivity analyses highlighted the influence of electricity sources, collection rates, and reprocessing yields on rSUD environmental performance. A comparison with economic input–output-based models revealed an alignment at the time between price differentials and LCA-derived GHG differences, though this may not always hold true. This review demonstrates the substantial environmental benefits of rSUDs, supporting their role as a readily achievable step towards more sustainable and circular healthcare systems. 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

The carbon tax is a crucial economic instrument for China; it aims to encourage the reduction of carbon emissions and provide additional revenue for the government in order to promote the transformation of society towards low-carbon and sustainable development. The suboptimal carbon tax refers to the carbon tax rate that achieves the best balance between emission reduction targets and economic benefits. Using China’s 2020 Non-competitive Input–Output Table, which encompasses 42 sectors, alongside carbon emission data sourced from the China Carbon Emission Accounts and Datasets (CEADs) covering 47 sectors, this study established a Carbon Tax-adjusted Input–Output Table of China’s Non-competitive Carbon Emissions 2020 (26 sectors) and constructed a multi-objective suboptimal carbon tax model based on an input–output price change model. Based on these, the suboptimal carbon tax rates under four different sets of constraints were simulated, including 49.2 CNY/ton (low inflation), 98.3 CNY/ton (low-to-medium inflation), 147.1 CNY/ton (medium-to-high inflation), and 195.5 CNY/ton (high inflation). We found that the suboptimal carbon tax should take into account its impact on prices, carbon reduction, and GDP, and higher carbon tax rates lead to more significant macroeconomic impacts and increased efforts in reducing emissions. Policy recommendations have also been put forward, such as launching a comprehensive research framework, establishing a synergistic and complementary mechanism between carbon taxation and carbon trading, designing a dynamic carbon tax, etc. Full article

In a competitive market, due to differences in the nature of various power generation entities, there is a decline in resource utilization and difficulties in ensuring a return on investment for generating units within the system. A bi-level non-cooperative game model based on the Unit Resource Return (URR) is proposed to safeguard the interests and demands of each power generation unit while improving the overall resource utilization rate of the system. Firstly, we construct a comprehensive energy-trading framework for the overall system and analyze the relationship between the Independent System Operator (ISO) and the generation units. Secondly, we propose the Unit Resource Return (URR), inspired by the concept of input-output efficiency in economics. URR evaluates the return on unit resource input by taking the maximum generation potential of each unit as the benchmark. Finally, a bi-level non-cooperative game model is established. In the lower-level non-cooperative game, the generating units safeguard their own interests, while in the upper-level, the ISO adjusts the output allocation and engages in a master–slave game between generating units to ensure the overall operational efficiency of the system. URR is adopted as the ISO’s price-clearing equilibrium criterion, enabling the optimization of both resource profitability and allocation. Ultimately, both the upper and lower-level decision variables reach a Nash equilibrium. The experimental results show that the bi-level non-cooperative game model based on the Unit Resource Return improves the overall resource utilization of the system and enhances the long-term operational motivation of the generating units. Full article

Climate change, especially extreme weather events, has significantly heightened the vulnerability of fisheries production supply chains. This study firstly investigates the input-driven climate risks through intermediate goods trade and their indirect impacts on China’s fisheries sector and constructs the Climate Risk-Trade-Production Model (CRTPM). Key findings include: (1) The input-driven climate risk indicator for China’s fisheries sector has increased over the period 1995–2020, with Brazil, Canada, the United States, Japan, South Korea, and Russia as major contributors. (2) From 1995 to 2020, rising climate risk index in Brazil and Canada negatively affected China’s fisheries output, with a 1% increase in climate risk index resulting in production declines of 0.173% and 0.367%, respectively. (3) In contrast, a reduction in the climate risk index in the United States and Japan lowered intermediate goods prices, boosting China’s output by 0.934% and 0.172%, respectively, for every 1% decrease in the climate risk index. (4) Climate risk index in South Korea and Russia, while initially increasing, eventually stabilized, having minimal impact on China’s fisheries production. It is the importance of monitoring extreme weather events to mitigate the economic vulnerabilities of China’s fisheries. Full article

This paper investigates the distributional drivers of carbon emissions in Türkiye, focusing on how household income and consumption patterns influence carbon footprints. Utilizing the microsimulation model, we integrate detailed expenditure data from Türkiye’s 2019 Household Budget Survey with the 2016 Input-Output table from the World Input-Output Database. This approach enables the simulation of both direct and indirect CO₂ emissions, providing a comprehensive analysis of the environmental impact of household consumption across different income groups. Our findings reveal significant disparities in carbon emissions, highlighting the complex interplay between income levels, consumption behaviors, and environmental outcomes. This research underscores the importance of considering distributional effects in the design of carbon pricing policies to ensure equity and effectiveness in emission reduction strategies. This study focuses on understanding household distributional drivers of carbon emissions. Full article

The economic impact of obtaining biomass could become significant to U.S. rural economies via the establishment of a bioeconomy. In 2023, the Bioenergy Technologies Office (BETO) and Oak Ridge National Laboratory provided a road map to obtain over a billion tons of biomass for conversion to bioenergy and other products. Using information from this roadmap, this study estimates the potential positive and negative economic impacts that occur because of land use change, along with increased technological advances. This is achieved by using the input–output model, IMPLAN, and impacting 179 Bureau of Economic Analysis regions in the conterminous United States. Biomass included in the analysis comprises dedicated energy crops, crop residues, and forest residues. The analysis found that managing pastures more intensively could result in releasing land to produce dedicated energy crops on 30.8 million hectares, resulting in the production of 361 million metric tons of biomass. This, coupled with crop residues from barley, corn, oats, sorghum, and wheat (162 million metric tons), plus forest residues (41 million metric tons), provide 564 million dry metric tons of biomass. Assuming the price for biomass in 2023 dollars was USD 77 per dry metric-ton, this additional production results in an economic benefit for the nation of USD 619 billion, an increase from the Business As Is scenario (Baseline) of almost USD 100 billion per year, assuming a mature biomass industry. An additional 700,000 jobs are required to grow, harvest/collect, and transport the biomass material from the land. Full article

Forecasting natural gas futures prices can help to promote sustainable global energy development, as the efficient use of natural gas as a clean energy source has become key to the growing global demand for sustainable development. This study proposes a new hybrid model for the prediction of natural gas futures prices. Firstly, the original price series is decomposed, and the subsequences, along with influencing factors, are used as input variables. Secondly, the input variables are grouped based on their correlations with the output variable, and different models are employed to forecast each group. A gated recurrent unit (GRU) captures the long-term dependence, an autoencoder (AE) downscales and extracts the features, and a multilayer perceptron (MLP) maps the complex relationships. Subsequently, random forest (RF) integrates the results of the different models to obtain the final prediction. The experimental results show that the model has a mean absolute error (MAE) of 0.32427, a mean absolute percentage error (MAPE) of 10.17428%, a mean squared error (MSE) of 0.46626, a root mean squared error (RMSE) of 0.68283, an R-squared (R²) of 93.10734%, and an accuracy rate (AR) of 89.82572%. The results demonstrate that the proposed decomposition–selection–prediction–integration framework reduces prediction errors, enhances the stability through multiple experiments, improves the prediction efficiency and accuracy, and provides new insights for forecasting. Full article

The energy stock price index maps the development trends in China’s energy market to a certain extent, and accurate forecasting of China’s energy market index can effectively guide the government to regulate energy policies to cope with external risks. The vector error correction model (VECM) analyzes the relationship between each indicator and the output, provides an external explanation for the way the indicator influences the output indicator, and uses this to filter the input indicators. The forecast results of the China energy stock price index for 2022–2024 showed an upward trend, and the model evaluation parameters MAE, MAPE, and RMSE were 0.2422, 3.5704% and 0.3529, respectively, with higher forecasting efficiency than other comparative models. Finally, the impact of different indicators on the Chinese energy market was analyzed through scenario setting. The results show that oscillations in the real commodity price factor (RCPF) and the global economic conditions index (GECON) cause fluctuations in the price indices of the Chinese energy market and that the Chinese energy market evolves in the same manner as the changes in two international stock indices: the MSCI World Index and FTSE 100 Index. Full article

The implementation of the Carbon Border Adjustment Mechanism (CBAM) carries profound implications for China’s export trade with the EU. However, a comprehensive analysis of CBAM’s impact on provincial export trade, particularly one grounded in industrial linkages and incorporating diverse policy scenarios, remains limited. To address this gap, this study develops a mechanistic framework based on industrial linkage theory and dynamically integrates key factors such as the scope of industries covered by CBAM, carbon emission accounting boundaries, and carbon pricing into a multi-scenario quantitative model. Leveraging a refined multi-region input–output (MRIO) model, we quantitatively assess the effects of CBAM on China’s provincial exports to the EU under various scenarios. The findings show that CBAM significantly raises export costs, leading to a pronounced decline in the competitiveness of five highly vulnerable industries. As CBAM expands to include sectors covered by the EU Emissions Trading System (EU ETS), the total levies on affected industries increase considerably, ranging from USD 0.07 billion to USD 2.25 billion depending on the scenario. Conversely, seven provincial industries, such as the chemical industry in Shanxi, experience only limited impacts due to their low direct carbon intensity and minimal overall increases in carbon tariffs. Then, the study underscores the pivotal role of China’s domestic carbon pricing mechanism in mitigating the effects of CBAM. Higher domestic carbon prices enhance China’s capacity to respond effectively, thereby reducing the overall impact of the mechanism. By adopting an inter-industry linkage perspective, this study provides new insights into assessing the multidimensional impacts of CBAM on China’s exports to the EU across provinces under different policy design scenarios, providing lessons for different categories of provinces on how to cope with CBAM. Full article

The question of whether microeconomic shocks induce aggregate fluctuations constitutes a central issue in economic research. This paper introduces a general equilibrium model with production networks to explore the propagation mechanisms of microeconomic shocks. A novel triangular production network structure is introduced, and simulations are performed using China’s input-output table to analyze the propagation of these shocks within the Chinese economy. The model demonstrates that the first-order effects of microeconomic shocks propagate downstream along the industrial chain, while the second-order effects of microeconomic productivity shocks propagate both upstream and downstream along the chain. The first-order propagation mechanism of microeconomic shocks involves changes in prices within the affected sector and its downstream sectors. Additionally, the second-order effects of microeconomic shocks rely on the reallocation of factors. The simulation results indicate that China’s production network matrix is triangular, and that the financial sector plays a crucial role in amplifying the effects of microeconomic shocks. Government should prioritize supporting upstream fundamental sectors to mitigate the adverse impacts of external shocks on economic fluctuations and to address systemic financial risks. Full article

This paper estimates the contribution of the hotel sector to the main macroeconomic figures of the Greek economy. For this purpose, we use a combination of input-output models as well as survey-based data. The analysis of the intersectoral relationships in the Greek economy indicates that the hotel sector constitutes a key sector of the economy. The total (direct and indirect) contribution of the hotel sector to the gross domestic product of the economy is estimated at 4.8% for the year 2023, whilst its total contribution to employment is estimated at 6.6%. Moreover, from the sectoral decomposition of the cost of the hotel product, it is found that almost 20% of the price of the final hotel product is composed of taxes, a percentage that is almost twice that of the other sectors of the economy. Finally, the evaluation of the results of the current study offers useful insights for policymakers. Full article

The stock market index future price forecasting is one of the imperative financial time series problems. Accurately estimated future closing prices can play important role in making trading decisions and investment plannings. This work proposes a new multi-output ensemble framework that integrates the hybrid systems generated through importance score based feature weighted learning models through a continuous multi-colony ant colony optimization technique (MACO-LD) for multi-day ahead index future price forecasting. Importance scores are obtained through four different importance score generation strategies (F-test, Relief, Random Forest, and Grey correlation). Multi-output variants of three baseline learning algorithms are brought in to address multi-day ahead forecasting. This study uses three learning algorithms namely multi-output least square support vector regression (MO-LSSVR), multi-output proximal support vector regression (MO-PSVR) and multi-output $\epsilon$-twin support vector regression (MO-$\epsilon$-TSVR) as the baseline methods for the feature weighted hybrid models. For the purpose of forecasting the future price of an index, a comprehensive collection of technical indicators has been taken into consideration as the input features. The proposed study is tested over eight index futures to explore the forecasting performance of individual hybrid predictors obtained after incorporating importance scores over baseline methods. Finally, multi-colony ant colony optimization algorithm is employed to construct the ensemble results from the feature weighted hybrid models along with baseline algorithms. The experimental results for all the eight index futures established that the proposed ensemble of importance score based feature weighted models exhibits superior performance in index future price forecasting compared to the baseline methods and that of importance score based hybrid methods. Full article