Carbon Emission Cost Research Articles

Low-carbon economic dispatch strategy for integrated power system based on the substitution effect of carbon tax and carbon trading

As the introduction of carbon peak and carbon neutrality, the intensity of carbon regulation is growing. The power system, as the main body of carbon emission, will face the constraints of compound carbon reduction policy. To address this issue, a low-carbon economic dispatch strategy for integrated power system based on the substitution effect of carbon tax and carbon trading is proposed to improve the economic and environmental joint benefits. Firstly, facing the volatility characteristics of carbon trading price, Monte Carlo method is implemented to analyze the uncertainty of carbon trading price. Next, the renewable energy power and electricity load are forecast by long short-term memory method. Subsequently, the gray wolves algorithm is imposed to optimize the dispatch model and obtain the optimal dispatch plan. The comparative analysis reveals that the carbon emission and dynamic payback period of the optimized integrated power system are 4.37 × 106 t and 10.06 years, which are reduced 1.67 × 105 t and 0.3 years compared with the conventional power system. Besides, the economic analysis demonstrates that the substitution effect can decrease the unit cost of carbon emission by 1.03 USD/t. Finally, the comparative analysis manifests that the proposed dispatch model can effectively boost the economic and environmental joint benefits for the integrated power system.

Integrated Sustainability Assessment of Electric Bus Adoption: A Comprehensive Model for Ecological, Economic, and Operational Impact

The spread of electric stations in cities around the world is an important step towards sustainable urban transportation. In Task 1,We developed the FE-VC model to determine carbon emission indicators based on the BWM approach, and demonstrated the ecological consequences over the life cycle through quantitative models. In the quantitative calculation of urban data, compared with diesel buses, the carbon emission reduction rate of pure electric buses is 28.68%, mainly from the fuel cycle, in which the carbon emission reduction rate of the fuel cycle stage is as high as 40.11%, and it takes about 3.5 years to achieve the relative carbon emission reduction. In Task 2, we construct an economic impact consequence model based on the time value of capital with the theory of time value of money as the core, consider the full life cycle of acquisition, operation and vehicle maintenance, quantify and visualize the impact mechanism of electric buses on the economy, and provide a method to understand the economic impact for metropolitan areas. For task 3, based on the optimal enterprise operating cost and the optimized economic impact model, we construct the initial replacement model of the electric bus. The initial replacement model was optimized by simplifying the social cost of carbon emission, and an electric bus replacement model based on the optimal enterprise operating cost was obtained. In city-specific applications, we find that vehicle choice has a significant impact on fleet replacement plans and operating costs, and that using multiple vehicles can significantly reduce operating costs as electrification rates increase.

Game‐based distributed energy‐sharing model for prosumers in microgrids considering carbon emissions and its fast equilibrium‐finding method

Game‐based distributed energy‐sharing model for prosumers in microgrids considering carbon emissions and its fast equilibrium‐finding method

Optimizing Cold Chain Distribution Routes Considering Dynamic Demand: A Low-Emission Perspective

Cold chain logistics, with its high carbon emissions and energy consumption, contradicts the current advocacy for a “low-carbon economy”. Additionally, in the real delivery process, customers often generate dynamic demand, which has the characteristic of being sudden. Therefore, to help cold chain distribution companies achieve energy-saving and emission-reduction goals while also being able to respond quickly to customer needs, this article starts from a low-carbon perspective and constructs a two-stage vehicle distribution route optimization model that minimizes transportation costs and refrigeration costs, alongside carbon emissions costs. This research serves to minimize the above-mentioned costs while also ensuring a quick response to customer demands and achieving the goals of energy conservation and emission reduction. During the static stage, in order to determine the vehicle distribution scheme, an enhanced genetic algorithm is adopted. During the dynamic optimization stage, a strategy of updating key time points is employed to address the dynamic demand from customers. By comparing the dynamic optimization strategy with the strategy of dispatching additional vehicles, it is demonstrated that the presented model is capable of achieving an overall cost reduction of approximately 17.13%. Notably, carbon emission costs can be reduced by around 17.11%. This demonstrates that the dynamic optimization strategy effectively reduces the usage of distribution vehicles and lowers distribution costs.

Robust optimal operation scheduling method for integrated energy system considering flexible energy storage and mixed hydrogen natural gas

ABSTRACT At present, the number of electric vehicles is increasing day by day. The full integration of electric vehicles and multi-energy coupled integrated energy systems is the future development trend. This paper introduces a novel form of flexible energy storage in the integrated energy system ;(IES): electric vehicle (EV). And It proposes that mixed natural gas hydrogen be used as the primary energy source for IES, which replaces traditional natural gas. Combined with the above, the IES is scheduled to meet the system’s multi-load for heating, cooling, and electricity demands. Based on the integrated energy system’s structure and energy conversion principles, a robust two-stage optimization model for the IES is proposed to minimize the daily operation scheduling costs. The proposed model considers the uncertainty of distributed renewable generation and flexible energy storage. Uncertain adjustment parameters are introduced into the model, which can flexibly adjust the conservatism of the scheme. Using the column and constraint generation algorithm (C&CG) and the strong duality theory, the model presented in this paper is solved and the optimal scheduling scheme is obtained. The final simulation results validate the effectiveness of the model and algorithm. The addition of flexible energy storage and mixed natural gas hydrogen effectively improves the economy and flexibility of the system while reducing carbon emissions costs. Consideration of uncertainty improves system robustness and safety. In addition, sensitivity analyses of the influence of the proportion of hydrogen mixed with natural gas, the setting of uncertain adjustment parameters, electricity prices, weather, and load fluctuations on system operation. The results show that compared to existing research, electric vehicles as a flexible energy storage device and mixed natural gas hydrogen supply, increase the economic benefits of IES by 19.8% and 23%, respectively.

Emission Reduction Decisions in Blockchain-Enabled Low-Carbon Supply Chains under Different Power Structures

With the global climate problem becoming increasingly severe, governments have adopted policies to encourage enterprises to invest in low-carbon technologies. However, the opacity of the carbon emission reduction process leads to incomplete consumer trust in low-carbon products as well as higher supply chain transaction costs. Based on this, this paper constructs Stackelberg game models with and without blockchain under different power structures and compares the impact of these models on low-carbon emission reduction decisions. The results show that: (1) blockchain does not necessarily improve enterprise profits and can only help enterprises maintain optimal profits within a certain range when the carbon emission cost is low; (2) when consumers’ environmental awareness is high, the blockchain can incentivize manufacturers to enhance carbon emission reduction, and it has an obvious promotional effect on retailers’ profits; and (3) the profit gap between enterprises in the supply chain is larger under different power structures, and the implementation of blockchain can coordinate profit distribution and narrow the gap between enterprises. Compared with the manufacturer-dominated model, the emission reduction in products is maximized under the retailer-dominated model. Our study provides theoretical support for the government to regulate greenhouse gas emissions as well as for the optimization of enterprises’ decision-making supported by blockchain.

Carbon emission and maintenance cost of commercial buildings: Quantification, analysis and benchmarking

Minimizing the carbon emissions of buildings entails effective resources deployment to maintain the buildings and their facilities. Through an environmental-economics lens, a mixed methods research was conducted on 27 commercial buildings, from which reliable and longitudinal data were collected. Considering the different mixes of premises (office, retail and car park) in the buildings and their variability in carbon emission intensity and maintenance cost intensity, a normalization method was developed based on appropriate scaling factors of the premises. Correlation analyses revealed significant correlations between building age and builder's work maintenance cost, as well as between building area and carbon emissions. The finding that the carbon emission intensities of the buildings decreased with capital project costs implies that capital projects, especially energy retrofits, can significantly reduce carbon emissions. Using the benchmarking charts constructed, the buildings - with or without capital projects implemented - were compared in terms of carbon emissions and maintenance costs. Besides contributing insights into future research, the study results hold significance for stakeholders, including policy makers, building owners and facilities managers, in optimizing maintenance resources to attain a cleaner built environment.

Bi-objective carbon-efficient distributed flow-shop scheduling with multistep electricity pricing

A bi-objective distributed flow-shop scheduling problem with multistep electricity pricing and carbon emissions is studied. One objective is to minimise the completion time of production, and the other is to minimise the total cost of multistep electricity pricing and carbon emissions. A mixed integer programming model and a two-stage knowledge based cooperative algorithm with a local reinforcement strategy are proposed for the problem. The extensive numerical experiments show that the two-stage algorithm was effective statistical significantly in generating non-dominated solution sets and Pareto frontiers. Simulations on Electricity Prices are applied to examine different multistep electricity pricing schemes, and management implications were drawn for both government and companies.

Addressing building related energy burden, air pollution, and carbon emissions of a low-income community in Southern California

This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10–20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17–22 %. Appliance electrification also reduces carbon emissions 5–10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.

Research on a Joint Distribution Vehicle Routing Problem Considering Simultaneous Pick-Up and Delivery under the Background of Carbon Trading

In order to explore the positive impact of the joint distribution model on the reduction in logistics costs in small-scale logistics enterprises, considering the demand on enterprises for simultaneous pick-up and delivery, as well as the cost of carbon emissions, this study considers the vehicle routing problem of simultaneous pick-up and delivery under a joint distribution model. First of all, an independent distribution model and a joint distribution model including fixed transportation, variable transportation, time penalty, and carbon emissions costs are established; second, by adding the self-adaption cross-mutation probability and the destruction and repair mechanism in the large-scale neighborhood search algorithm, the genetic algorithm is improved to adapt to the solution of the model in this paper, and the effectiveness of the improved algorithm is verified and analyzed. It is found that the improved genetic algorithm is more advantageous than the original algorithm for solving the problems of both models designed in this paper. Finally, the improved genetic algorithm is used to solve the two models, and the results are compared and analyzed. It is found that the joint distribution model can reduce the total cost by 6.61% and the carbon emissions cost by 5.73%. Additionally, the impact of the carbon trading mechanism on the simultaneous pick-up and delivery vehicle routing problem under the joint distribution model is further explored. The results of this study prove that enterprises can effectively reduce costs, improve profits, reduce carbon emissions, and promote the sustainable development of logistics enterprises under the condition of joint distribution.

Environmental and Economic Assessment of Membrane Capacitive Deionization (MCDI) and Low-Pressure Reverse Osmosis (LPRO) for Sustainable Irrigation in the Mediterranean Region

Ensuring the sustainability of a product or a system requires a thorough evaluation of its environmental and socioeconomic impacts. In this context, one of the objectives of the EU-PRIMA SmaCuMed project is to evaluate the socioeconomic and environmental impacts of the Smart Cube system. The Smart Cube was developed for the PV-powered desalination of brackish groundwater with membrane capacitive deionization (MCDI) and low-pressure reverse osmosis (LPRO); it additionally uses smart sensors for controlled irrigation in remote agricultural areas in Morocco, as an example for the North African region. Based on the Life Cycle Sustainability Assessment approach, this paper aims to assess the environmental and economic impacts of the Smart Cube, using Life Cycle Costing (LCC) and Life Cycle Assessment (LCA) analyses for environmental evaluation. Various scenarios have been defined for both environmental and economic assessments. Based on 1 m3 of produced desalinated water, the LCC results showed that, when using the desalination technologies directly connected to the grid, the prices are lower than those obtained when it was supplied by the PV system. This is only due to the very low energy prices from the Moroccan grid (EUR 0.10/kWh). The LCC results showed that LPRO is a more cost-effective option for producing desalinated water, with a lower total cost compared to MCDI. However, LCA results indicated that LPRO has a higher environmental impact compared to MCDI. If higher water production capacity is a priority, MCDI connected to PV is the best choice, with lower carbon emission but higher overall water costs.

Alcohol's contribution to climate change and other environmental degradation: a call for research.

Climate change is the single biggest health threat facing humanity. The production, distribution and consumption of many fast-moving consumer goods contribute substantially to climate change, principally through releasing greenhouse gas emissions. Here we consider just some of the ways that alcohol-already a key contributor to an array of health, social and economic burdens-exacerbates environmental harms and climate change. We explore current evidence on alcohol production as a resource- and energy-intensive process, contributing to significant environmental degradation through water usage and other carbon emission costs. We argue that the impacts of alcohol production on climate change have been minimally explored by researchers. Yet the extent of the unfolding catastrophe beholds us to consider all available ways to mitigate unnecessary emissions, including from products such as alcohol. We then turn to suggestions for a research agenda on this topic, including investigations of commercial determinants, inequalities and product advice to help consumers choose lower-carbon options. We conclude by arguing that public health researchers already have an array of methodological expertise and experience that is well placed to produce the evidence needed to inform regulation and efforts by alcohol producers and consumers to minimize their contributions to environmental harms.

Robust optimal dispatch strategy of integrated energy system considering CHP-P2G-CCS

Integrated energy systems (IESs) can improve energy efficiency and reduce carbon emissions, essential for achieving peak carbon emissions and carbon neutrality. This study investigated the characteristics of the CHP model considering P2G and carbon capture systems, and a two-stage robust optimization model of the electricity-heat-gas- cold integrated energy system was developed. First, a CHP model considering the P2G and carbon capture system was established, and the electric-thermal coupling characteristics and P2G capacity constraints of the model were derived, which proved that the model could weaken the electric-thermal coupling characteristics, increase the electric power regulation range, and reduce carbon emissions. Subsequently, a two-stage robust optimal scheduling model of an IES was constructed, in which the objective function in the day-ahead scheduling stage was to minimize the start-up and shutdown costs. The objective function in the real-time scheduling stage was to minimize the equipment operating costs, carbon emission costs, wind curtailment, and solar curtailment costs, considering multiple uncertainties. Finally, after the objective function is linearized with a ψ-piecewise method, the model is solved based on the C&CG algorithm. Simulation results show that the proposed model can effectively absorb renewable energy and reduce the total cost of the system.

Optimal Scheduling Considering Carbon Capture and Demand Response under Uncertain Output Scenarios for Wind Energy

In light of the uncertainties associated with renewable energy sources like wind and photovoltaics, this study aims to progressively increase their proportion in the energy mix. This is achieved by integrating carbon capture devices into traditional thermal power plants and enhancing demand-side management measures, thereby advancing low-carbon objectives in the energy and electricity sectors. Initially, the research proposes utilizing the K-means clustering algorithm to consolidate and forecast the fluctuating outputs of renewable energies such as wind and photovoltaics. Further, it entails a comprehensive analysis of low-carbon resources on both the supply and demand sides of the electricity system. This includes installing carbon storage and power-to-gas facilities in carbon capture plants to create a versatile operating model that can be synchronized with wind power systems. Additionally, the limitations of carbon capture plants are addressed by mobilizing demand-side response resources and enhancing the system’s low-carbon performance through the coordinated optimization of supply and demand resources. Ultimately, this study develops an integrated energy system model for low-carbon optimal operation, aimed at minimizing equipment investment, carbon emission costs, and operational and maintenance expenses. This model focuses on optimizing the load and supply distribution plans of the electrical system and addressing issues of load shedding and the curtailment of wind and solar power. Validation through three typical scenarios demonstrates that the proposed scheduling method effectively utilizes adjustable resources in the power system to achieve the goal of low-carbon economic dispatch.

Multi-objective optimal scheduling considering low-carbon operation of air conditioner load with dynamic carbon emission factors

As global temperatures rise and climate change becomes more severely. People realize that air conditioning systems as a controllable resource and play an increasingly important role in reducing carbon emissions. In the past, the operation optimization of air conditioning systems was mainly oriented to user comfort and electricity costs ignoring the long-term impact on the environment. This article aims to establish a multi-objective model of air-conditioning load to ensure user temperature comfort performance and reduce the total cost (i.e., electricity cost and carbon emission cost) simultaneously. Multi Sand Cat Swarm Optimization (MSCSO) algorithm combined with gray target decision-making (GTD) is used to explore optimal solution. Meanwhile four competitive strategies are applied to validate the effectiveness of the proposed method, i.e., genetic algorithm (GA), MSCSO-comfort objective, MSCSO-total electricity cost objective and unoptimization. The simulation results show that the MSCSO-GTD based objective method can significantly reduce total costs while taking into account appropriate indoor temperature comfort.

Examining the Social Costs of Carbon Emissions and the Ecosystem Service Value in Island Ecosystems: An Analysis of the Zhoushan Archipelago

Assessments of the ecosystem service value (ESV) and the social cost of carbon (SCC) inform national and government management decisions in the areas of human well-being and climate change mitigation and adaptation, respectively. Studying the correlation between the two provides a theoretical basis for low-carbon and high-quality regional development, achieving economic decarbonization, and improving human well-being. In this study, we take Zhoushan Archipelago as a case study, consider the ESV and SCC in Zhoushan Archipelago during the period 2010–2020, analyze their spatial development characteristics, and analyze the correlation between the two in time and space. The findings indicate that, with only a 1.5% change, the overall ESV in the Zhoushan Archipelago fell between 2010 and 2020. Conversely, there was a 1604.01 × 104 t increase in net carbon emissions and a quick 2452% increase in SCC. During the study period, a substantial positive association was found between ESV and SCC in the Zhoushan Archipelago, according to the global spatial correlation analysis of the two variables. It passed the test for p-value. This study presents a new potential way to solve the environmental and economic difficulties caused by climate change by providing a mechanism for quantitatively assessing the environment from the perspective of monetary worth. In order to improve the ecological security pattern and ease the burden of regional carbon emissions, it is vital to make use of regional advantages, maintain forests, and develop blue-carbon resources such as mudflats. It is a good idea to cooperate regionally with nearby metropolitan agglomerations. The study’s findings are crucial for advancing sustainable development planning in the Zhoushan Archipelago, both theoretically and practically.

Application Of Sparrow Search Algorithm in Cold Chain Low Carbonization Logistics

Driven by consumer upgrades and national policies, the cold chain logistics industry has experienced rapid development, accompanied by increased expenditure of energy and carbon emissions. To achieve energy savings, emission reductions, and overall cost reduction in the cold chain logistics industry, this article establishes an optimization model for optimizing transportation routes, taking into account carbon emission costs, refrigeration costs, and time penalty costs. The objective is to comprehensively consider reducing refrigeration costs and time penalty costs throughout the entire phase of cold chain logistics, aiming to minimize the total cost for logistics companies during actual delivery processes. The improved Sparrow Search Algorithm (SSA) is employed to solve the optimization problem. The results of the case study analysis validate the applicability and superiority of the improved SSA for route optimization problems, as it demonstrates fast convergence speed and strong search capabilities.

Multimodal transportation routing optimization based on multi-objective Q-learning under time uncertainty

Multimodal transportation is a modern way of cargo transportation. With the increasing demand for cargo transportation, higher requirements are being placed on multimodal transportation multi-objective routing optimization. In multimodal transportation multi-objective routing optimization, in response to the limitations of classical algorithms in solving large-scale problems with multiple nodes and modes of transport, the limitations of directed transportation networks in the application, and the uncertainty of transport time, this paper proposes an optimization framework based on multi-objective weighted sum Q-learning, combined with the proposed undirected multiple-node network, and characterizes the uncertainty of time with a positively skewed distribution. The undirected multiple-node transportation network can better simulate cargo transportation and characterize transfer information, facilitate the modification of origin and destination, and avoid suboptimal solutions due to the manual setting of wrong route directions. The network is combined with weighted sum Q-learning to solve multimodal transportation multi-objective routing optimization problems faster and better. When modeling the uncertainty of transport time, a positively skewed distribution is used. The three objectives of transport cost, carbon emission cost, and transport time were studied and compared with PSO, GA, AFO, NSGA-II, and MOPSO. The experimental results show that compared with PSO, GA, and AFO using a directed transportation network, the proposed method has a significant improvement in optimization results and running time, and the running time is shortened by 26 times. The proposed method can better solve the boundary of the Pareto front and dominate the partial solutions of NSGA-II and MOPSO. The effect of time uncertainty on the performance of the algorithm is more significant in transport orders with high time weight. With the increase in uncertainty, the reliability of the route decreases. The effectiveness of the proposed method is verified.

Bi-level optimization configuration method for microgrids considering carbon trading and demand response

Under the background of the shortage of traditional energy and the increasingly serious environmental problems, this paper proposes a bi-level optimization configuration method for microgrids based on stepped carbon trading and price-based demand response. The stepped carbon trading mechanism is introduced into the planning model to clarify the impact of construction processes on land carbon emissions. Simultaneously, price-based demand response during system operation is introduced, guiding users to change the electricity consumption strategies to promote the consumption of renewable energy. Then, a bi-level optimal configuration framework is constructed. The upper level is optimized to allocate the capacity of the microgrid, with the lowest annual equivalent comprehensive cost as the objective function. The lower level is optimized for microgrid operation, with the minimum sum of the microgrid operation cost and carbon emission cost as the objective function. In view of the advantages of enhanced whale optimization algorithm (E-WOA) in solving multi-dimensional feature selection problems, E-WOA and CPLEX solver are combined to solve the bi-level optimization model for the first time. Simulation results show that after introducing a stepped carbon trading mechanism, the system carbon emissions decreased by 23.09%. Considering price-based demand response under the premise of introducing a stepped carbon trading mechanism, the total cost decreased by 3.57% and the carbon emissions decreased by 19.83%.

Research on Highway Maintenance and Management Cost Based on Life Cycle Cost Analysis

With the rapid development of China’s highway construction, the huge demand for funds and the lack of financial strength of the contradiction is increasingly obvious, can effectively control the maintenance costs, highway operators to obtain sustainable development advantages of the key factors. This paper measures the highway maintenance and management costs including carbon emission costs based on the perspective of life cycle cost analysis. Based on the perspective of life cycle cost analysis, prediction models are developed for each component cost of highway maintenance and management costs. The carbon emission costs generated during the operation duration of highways are also included in the life-cycle costs, and the calculation model is proposed for the strategy of “30∙60 carbon target” in China. In the example analysis, the carbon emission cost of new energy vehicles is only 3/4 of that of fuel vehicles in the driving phase; more than 90% of the carbon emission is generated by the driving vehicles.