Carbon Constraints Research Articles

Measurement on the Efficiency of Regional Logistics Industry under the Constraint of Low Carbon

The traditional efficiency measurement of the logistics industry does not consider the problem of dioxide emissions. This paper proposes the total factor productivity (CLP) of the logistics industry under the constraint of carbon intensity. Based on the construction of the panel data of the logistics industry in 30 provinces and regions in China from 2005 to 2012, the environmental DEA technology and the directional distance function method are applied to conduct an empirical analysis of the growth sources and regional differences of the total factor productivity of China's logistics industry under the constraints of carbon intensity. . The results show that the logistics industry's total factor productivity under the carbon intensity constraint has an average annual growth rate of 1.9%, which is higher than that without considering the carbon constraint; the logistics industry productivity index under the carbon intensity constraint is consistent with the carbon intensity target, and the logistics industry's total factor productivity has improved , The carbon intensity decreases; the improvement of logistics productivity mainly depends on the progress and innovation of logistics technology; under the constraint of carbon intensity, there is convergence in the logistics productivity of the eastern region, the difference in the central region is expanding, and the western region is shrinking.

Adaptive genetic algorithm for scheduling problem in flexible workshop with low carbon constraints

Taking the completion time, energy carbon emission and total machine load as independent time factors into consideration, a flexible workshop scheduling model is established to minimise the maximum completion time, energy emissions and the total machine load. The population could be initialised by greedy algorithm and random number method, and this model could be solved by the crossover probability and genetic probability adaptive method. The feasibility and effectiveness of the improved genetic algorithm are verified by testing the data set and comparing several single-objective data and normalised multi-objective data.

Adaptive genetic algorithm for scheduling problem in flexible workshop with low carbon constraints

Taking the completion time, energy carbon emission and total machine load as independent time factors into consideration, a flexible workshop scheduling model is established to minimise the maximum completion time, energy emissions and the total machine load. The population could be initialised by greedy algorithm and random number method, and this model could be solved by the crossover probability and genetic probability adaptive method. The feasibility and effectiveness of the improved genetic algorithm are verified by testing the data set and comparing several single-objective data and normalised multi-objective data.

Research progress on logistics network optimization under low carbon constraints

In this paper, a literature review was conducted on the optimization of logistics network under low carbon constraints. This paper summarized the basic research process of logistics network optimization, and discussed the basic problems of logistics network optimization (distribution center location and vehicle distribution route optimization) and the compound optimization problems of logistics network (forward logistics network optimization and reverse logistics network optimization). In this paper, we mainly discuss the mathematical model constructed by one or more of the goals of minimum carbon emissions, minimum sum of total costs, minimum total delivery time and minimum total delivery distance, and the solving strategies using heuristic algorithms such as particle swarm optimization, genetic algorithm, and ant colony algorithm. It provided the latest technology in this field for the researchers of enterprise logistics network model and points out the future development trend.

A novel optimization model for integrating carbon constraint with demand response and real-time pricing

Since the global warming has recently become more severe causing many serious changes on the weather, economy, and society worldwide, lots of efforts have been put forward to prevent it. As one of the most important energy sectors, improvements in electric power grids are required to address the challenge of suppressing the carbon emission during electric generation especially when utilizing fossil-based fuels, while increasing the use of renewable and clean sources. This paper hence presents a novel optimization model for tackling the problems of optimal power scheduling and real-time pricing in the presence of a carbon constraint while taking into account a demand response possibility, which may provide a helpful method to limit the carbon emission from conventional generation while promoting renewable generation. The critical aspects include explicitly integrating the cost of emission with the total generation cost of conventional generation and combining it with the consumer satisfaction function. As such, conventional generation units must carefully schedule their power generation for their profits, while consumers, with the help from renewable energy sources, are willing to adjust their consumption to change the peak demand. Overall, a set of compromised solution called the Pareto front is derived upon which the conventional generating units choose their optimal generation profile to satisfy a given carbon constraint.

Global CO2 impacts of light-duty electric vehicles

Electric vehicles (EV) offer a solution for decarbonization of transport. We evaluate global scenarios of EV deployment and their impacts on total global CO2 emissions. For this assessment, we enhance the MIT Economic Projection and Policy Analysis (EPPA) model to represent the fleet dynamics of light-duty vehicles (LDV), including internal combustion engine (ICE) vehicles and EVs. For EV fleet, both plug-in hybrid vehicles (PHEV) and battery electric vehicles (BEV) are considered. We consider several illustrative scenarios and find that global LDV stock is projected to grow from 1.1 billion vehicles in 2015 to 1.65–1.75 billion in 2050, while global EV stock is growing from about a million in 2015 to about 585–825 million in 2050. At this level of market penetration, EVs would constitute one-third to one-half of the overall LDV fleet by 2050 in different scenarios, with the stricter carbon constraints implied in the Paris to 2 °C scenario leading to the largest EV share. Our modeling suggests that EV uptake will vary across regions. China, the U.S., and Europe remain the largest markets in our study timeframe, but the EV presence is projected to grow in all regions. While the global LDV fleet grows by about 50% by 2050, the corresponding CO2 emissions from LDV are reduced by about 50% in 2050 relative to 2015. Global carbon intensity of LDVs are reduced by about 70% from 2015 to 2050.

Regional logistics efficiency and performance in China along the Belt and Road Initiative: The analysis of integrated DEA and hierarchical regression with carbon constraint

Although some studies have focused on the evaluation of regional logistics efficiency in China along the Belt and Road Initiative (B&R Initiative), there exist research gaps pertaining to systematically exploring the effects of internal and external influencing factors on the efficiency, economic performance and environmental performance of regional logistics industry for different regions and time periods. Therefore, in this paper, the integration of the slack-based measure (SBM) model and hierarchical regression is developed to perform an 11-year dynamic analysis (2007–2017) with carbon emission constraint. The improvement directions for inefficient regions are provided by benchmark analysis. The decrement rate and increment rate are proposed to further reveal the redundancy or shortage degree of indicators. Our results reveal the large imbalance in logistics efficiency among China’s regions along the B&R Initiative. However, the gaps in efficiency between the eastern and western regions have narrowed since the launch of the B&R Initiative. With regression analysis, we find whether in regions along the Silk Road Economic Belt or the Maritime Silk Road, whether from 2007 to 2012 or from 2013 to 2017, the specialization degree of logistics industry plays a prominently positive role in logistics efficiency and a negative role in environmental performance. It is interesting to find the opposite effect of opening up level on efficiency between regions along the Maritime Silk Road (positive effect) and the Silk Road Economic Belt (negative effect). We find the seesaw effect of economic development level and government regulation on economic performance and environmental performance for the southwest region. In addition, from 2013 to 2017, the external factors that have a positive effect on efficiency have changed from the opening up level before 2013 to the regional economic development level.

An inventory model of a deteriorating product considering carbon emissions

This paper studies an inventory optimization problem involving perishable products under carbon policy. It considers that the market demand for perishable products is deterministic and it is related to selling price and stock level and that the ordering and storing of perishable products can create carbon emissions. Additionally, preservation technology can change perishable item’s deterioration rate is also taken into account. For maximizing the retailer’s profit in the finite planning period, inventory models under a carbon tax policy and under a carbon cap-and-trade policy are developed. Firstly properties of the optimal solutions of two inventory models are analysed. Next algorithms are designed to solve the two inventory models and the optimal solutions of the inventory models without carbon constraint, under the two carbon policies are compared. Finally, the theoretical properties of the models are demonstrated by numerical examples, sensitivity analysis of the main model parameters is carried out, and the influence of the parameters on the retailer’s inventory policy is discussed.

The effect of investment in green technology in a two echelon supply chain under strict carbon-cap policy

PurposeStrict carbon-cap policy is one of the basic policies proposed by the regulatory bodies to reduce the anthropogenic greenhouse gas emission. The purpose of this paper is to examine whether it is beneficial for a company to invest in green technology or not under the strict carbon-cap policy and for that a two echelon supply chain model is developed. This paper gives insight about judicious decision about investment on green technology.Design/methodology/approachMathematical modeling approach has been adopted to understand the effect of investment on green technology. All the cost and emissions parameters have been derived and the total cost (TC) and total emission equations have been formulated mathematically. Two constrained mixed-integer nonlinear programming (MINLP) problems have been formulated and solved considering with or without green investment. Further, supply chain cost is optimized without carbon constraint to understand the effect of carbon constraint.FindingsThe investment in green technology can reduce the total supply chain cost. The study reveals that handling different parameters optimally can reduce both cost and emissions.Originality/valueThis paper tries to assess the effectiveness of green investment on technology under strict carbon-cap policy on a supply chain and, thereby, added value to the existing work. It examines the role played by various parameters under strict carbon-cap policy to draw insights, which will be beneficial for the academic community and managers.

Research on the dynamic linkage among the carbon emission trading, energy and capital markets

Facing the huge pressure of emission reduction, China officially launched a national carbon emission trading system at the end of 2017. The power industry is an important breakthrough in the construction of China's carbon emission trading system. To push the optimization of the power structure through carbon constraint is the necessary path to promote the transformation of the power industry from fossil energy to clean and low-carbon energy. From the perspective of power consumption, this paper constructs the linkage mechanism model of China's carbon emission trading market, energy market and capital market and applies the DCC-MVGARCH model to analyze the dynamic linkage mechanism of the three markets. The results show that the price fluctuation of the yield series of products in each market has clustering characteristics, and the dynamic correlation has significant time-varying characteristics and persistent characteristics. The dynamic conditional correlation between the carbon emission trading market and the oil market and between the carbon emission trading market and the natural gas market are basically consistent. The dynamic correlation coefficient between the carbon emission trading market and the capital market is negative, which is related to the unreasonable quota setting in China's carbon emission market. On the basis of empirical analysis, the optimal balance weight and the optimal hedging ratio of each asset are determined.

Robust nitrate removal and bioenergy generation with elucidating functional microorganisms under carbon constraint in a novel multianode tidal constructed wetland coupled with microbial fuel cell

This study investigated synthetic wastewater treatment under low inflow C/N ratio and characterized NO3−–N-transforming and electricity-producing bacteria in a multi-anode tidal constructed wetland–microbial fuel cell (TFCW–MFC). The optimal concurrent average removal rates of NH4+–N and NO3−–N were 73% and 78%, respectively, under a flood/rest/flood time of 4 h/2h/4h in “tide” mode accompanied by one recirculation. The lowest NO3−–N concentration among all anodes was observed when the electrode gap was 45 cm. Similarly, the 45 cm anode exhibited selective enrichment of Variovorax and Azoarcus. Correction analysis showed that the high relative abundance of Azoarcus was crucial in enhancing NO3−–N removal, and the internal resistance significantly decreased as the relative abundance of Acidovorax increased. These results suggest that NO3−–N removal and bioelectricity generation can be promoted in a TFCW–MFC with limited carbon by improving the culture conditions for specific genera.

Border Carbon Adjustments in a Post-Paris World: Same Old, Same Old, but Different?

With the adoption of the Paris Agreement in 2015, nations ushered in a new era of international climate change cooperation. To meet the Agreement’s goals, countries need to adopt increasingly ambitious national emissions control pledges. While the Agreement covers all countries, ambition levels and the implementation of policy measures will likely vary. Any unevenness in ambition or implementation will increase pressure on policy makers to prevent loss of competitiveness, relocation of carbon-intensive production to other countries (carbon leakage), and free rider behavior. Addressing these issues will likely require measures providing some form of remedy for producers subject to carbon constraints. This paper focuses on one such proposal: border carbon adjustments, which are charges levied on the greenhouse gases emitted during the production of a good. In recent years, policy makers have increasingly pushed for the development of a carbon charge imposed at the border. However, if border adjustments are implemented, policy makers need to address concerns about violating international trade law. This paper outlines how such a design could be developed. First, we introduce the concept of border carbon adjustments and explain how they have featured in policy discussions thus far. We then examine the compatibility of border carbon adjustments with World Trade Organization (WTO) law. Finally, we outline several design principles that could guide the adoption and implementation of such measures.

Implications of Future Natural Gas Demand on Sugarcane Production, Land Use Change and Related Emissions in Brazil

Due to its low share of energy-related emissions, energy systems models have overlooked the implications of technological transition in the agricultural sector and its interaction in the wider energy system. This paper explores the role of agriculture intensification by using a novel agricultural-based energy systems model. The aim is to explore the future role of Brazil’s agriculture and its dynamics with other energy sectors under two carbon constraint scenarios. The main focus has been to study resource competition between sugarcane and natural gas at a country level. Results show that in order to meet the future food and bioenergy demand, the agricultural sector would start intensifying by 2030, improving productivity at the expense of higher energy demand; however, land-related emissions would be minimised due to freed-up pasture land and reduction in deforestation rates. Additionally, the development of balanced bioenergy and natural gas markets may help limit the sugarcane expansion rates, preserving up to 12.6 million hectares of forest land, with significant emissions benefits.

Skeleton model-based product low carbon design optimization

With the diversification and customization of product design route, low-carbon design optimization has become a new method to determine the optimal product parameters. Numerical optimization is an effective tool in the analysis of engineering system. As it is crucial to analyze the behaviour of the products with the optimization of complex products, many valuable contributions are obtained. The current researches always focus on optimization design in detail design. However, at the early stage of design process, it could not be obtained with detailed information, and the rough and uncertain information is always gradual in the design process. This paper is devoted to product low carbon design optimization based on the skeleton model under the condition of insufficient information at the early design stage. After the design principle for compliant exoskeleton robotics is discussed, the parameter optimization based on skeleton model under low carbon constraints is then proposed. The exoskeleton robotics with the consideration of compliant mechanism is given as an example which demonstrates that the methodology is helpful to skeleton model-based low carbon optimization approach, which facilitates the development of customized exoskeleton robots.

Constraining fossil fuels based on 2 °C carbon budgets: the rapid adoption of a transformative concept in politics and finance

This article traces how the notion of finite limits to emissions based on 2 °C carbon budgets was applied to increase the credibility of a carbon-constrained future in two very separate realms, social movements contesting fossil-fuel development and the financial sector—a process yet to be described in the relevant political and financial literatures. For each realm and sub-areas within them, we apply a three-wave taxonomy to trace the application of 2 °C carbon budget based concepts—including stranded assets, the carbon bubble, divestment, and anti-pipeline campaigns—from the fringe to the mainstream in under 10 years. We do so by drawing on relevant primary documents and peer-reviewed literature, complemented by a quantitative textual analysis of relevant discourse from news sources. The article establishes how, in efforts to shift expectations, climate proponents used 2 °C carbon budgets to frame a stark choice between a safe climate with strict carbon constraints and growth-oriented fossil-fuel interests. The article also demonstrates that these concepts, and efforts inspired by them, contributed to constraints on fossil-fuel developments and interests, arguably further enhancing the credibility of a carbon-constrained future. We conclude with a discussion of the potentially self-reinforcing nature of such expectation dynamics and by highlighting overlapping implications for actors across finance, where investors reorient risk assessment around climate, and social movements, where activists disrupt states’ entrenched commitment to fossil-fuel expansion.

Study on substitutable value of electric heating instead of coal heating in northern China under carbon constraints

Northern China has long been puzzled by smog in winter. Electric heating, as a kind of clean heating method, has been promoted to replace coal-fired heating. However, owing to the power structure dominated by coal power in China, electric heating may cause more carbon emissions than that of coal-fired heating if the scale of the former exceeds relative limit. It is essential to study the electric heating area development limit to ensure carbon reduction compared with that of coal-fired heating. In this paper, based on the analysis of carbon emissions from coal-fired heating and electric heating, a calculation model for the substitutable value of electric heating instead of coal heating is established under carbon constraints. The model reveals that the substitutable value is related to the total heating area and the power structure. The Long Short-Term Memory Neural Network forecasting model is constructed to forecast total central heating area in urban areas of northern China from 2020 to 2035. The baseline scenario (BS), low carbon scenario (LCS) and enhanced low carbon scenario (ELCS) are set to forecast the power structure in northern China. The results show that under the BS, the substitutable value of electric heating instead of coal heating can reach 15.058 billion square meters by 2035. Under the LCS and ELCS, the substitutable value will be even greater by 2035, 18.726 and 25.080 billion square meters, respectively. In addition, when the proportion of thermal power generation is less than 49.10%, electric heating per square meter can make less carbon emissions than coal-fired heating per square meter. The findings provide a reference for medium and long-term planning of large-scale electric heating in northern China.

Spatially-resolved urban energy systems model to study decarbonisation pathways for energy services in cities

This work presents the COMET (Cities Optimisation Model for Energy Technologies) model, a spatially-resolved urban energy systems model that takes into account energy service demands for heating, cooling, electricity, and transport, and finds cost-effective pathways for supplying these demands under carbon constraints, trading-off energy supply, network infrastructure, and end-use technologies. Spatially-resolved energy service demands were obtained for the city of Sao Paulo, and six scenarios were modelled. Results show that district cooling is cost-effective in the highest linear cooling density zones, with full penetration in zones with over 1100 kWh/m by 2050. This threshold diminishes with tighter carbon constraints. Heating is electrified in all scenarios, with electric boilers and air-source heat pumps being the main supply technologies for the domestic and commercial sectors respectively by 2050. In the most carbon constrained scenario with a medium decarbonised electricity grid, ground source heat pumps and hydrogen boilers appear as transition technologies between 2030 and 2045 for the commercial and domestic sectors respectively, reaching 95% and 40% of each sector’s heat installed capacity in 2030. In the transport sector, ethanol cars replace gasoline, diesel, and compressed natural gas cars; compressed natural gas buses replace diesel and electric buses; and lorries continue using diesel. In carbon constrained scenarios, higher penetrations of electric cars and buses are obtained, while no change is observed for lorries. Finally, the most expensive scenario was only 6% more expensive than the reference scenario, meaning that achieving decarbonisation targets is not much costlier when comparing scenarios from a system-wide perspective.

An Optimal Ordering Decision-making Model with Random DemandUnder Carbon Constraint

Under the increasing pressure to reduce carbon emission, the enterprises need to actively take account into carbon emission, and take low carbon action among daily business activities. This not only relate to the realization of carbon emission goal, but probably efficient solution to our country carbon emission targets. Applying to optimal theory under carbon-constrained, this paper comprehensively include economical cost and environment cost to construct random optimal decision-making model. Then using mat lab numerical analysis, this paper reveals the decision-making mechanism of enterprise ordering making under carbon cap constrained, and provides management implications and future research direction. The result indicates that: An enterprise can significantly reduce carbon emission without significantly increasing cost through adjusting ordering quantity. Enterprise’s carbon emissions show a certain correlation with order quantity when carbon emission cap is within the scope of threshold value, but optimal ordering decision-making has no correlation with carbon emission cap when cap is beyond the scope of threshold value. Caron emission cap is set voluntarily by an enterprise’s decision maker or put forward mandatorily by an external regulatory agency, and its setting should be reasonable and scientific.

Strategic Storage Use in a Hydro-Thermal Power System with Carbon Constraints

Several interconnected power systems worldwide have largely thermal and hydro production along with carbon cap-and-trade (C&T) systems and variable renewable energy sources (VRES). C&T policies increase VRES generation, and socially optimal storage deployment could integrate VRES output. However, hydro reservoirs may be used strategically due to market power. We investigate these distortions and assess measures for their mitigation via a bottom-up equilibrium model of New York and Quebec. In particular, we find evidence that hydro producers shift water between seasons to manipulate electricity prices even under a net-hydro production constraint. Alternative regulation covering net imports as well as net-hydro production limits such temporal arbitrage but enables firms with both thermal generation and pumped-hydro storage to exercise spatial arbitrage. We demonstrate that these distortions will be exacerbated under more stringent C&T policies because price-taking thermal producers are less able to respond to price signals.

Energy conversion path and optimization model in COVID-19 under low carbon constraints based on statistical learning theory

This paper uses statistical learning theory and big data analysis to study the energy consumption structure of China from qualitative and quantitative aspects during COVID-19. According to the domestic and foreign scholars’ research on the optimization of energy consumption structure, the carbon emission factor is considered in the optimization of energy consumption structure. Taking the minimum energy consumption cost and carbon dioxide emission as the objective function, the carbon dioxide emission is taken as the objective function, and the total energy consumption and various energy consumption proportions as the constraint conditions, the multi-objective planning method is used to evaluate the energy consumption structure of China. The optimization model of source consumption structure is analyzed, and the medium and long-term energy transformation path and optimization model under low-carbon constraints are studied. Combined with the experimental algorithms related to big data, it is concluded that China’s economic development mainly depends on a large amount of energy consumption during the COVID-19 period. On this basis, some suggestions are put forward to realize the sustainable development of China’s economy and energy.