Inventory Routing Problem with Carbon Emission Consideration

Generally, the inventory routing problem occurs in a supply chain where customers consider the supplier responsible for inventory replenishment. In this situation, the supplier finds the answer to questions regarding the time and quantity of delivery to the customer as well as the sequence of customers in the routes. Considering the effect of production decisions on answering these questions, the present paper examines the integrated decision making on production, routing and inventory in a two-echelon supply chain composed of a manufacturer and multiple retailers. Transshipment, as a policy in supply chain logistic which increase integration and decrease inventory cost, is also allowed between retailers. The mathematical formulation for the problem is developed and an adaptive large neighborhood search heuristic is proposed to solve this complicated problem. The results of numerical experiments show that the solutions yielded by the heuristic method have high efficiency.

Better information on greenhouse gas (GHG) emissions and mitigation potential in the agricultural sector is necessary to manage these emissions and identify responses that are consistent with the food security and economic development priorities of countries. Critical activity data (what crops or livestock are managed in what way) are poor or lacking for many agricultural systems, especially in developing countries. In addition, the currently available methods for quantifying emissions and mitigation are often too expensive or complex or not sufficiently user friendly for widespread use.The purpose of this focus issue is to capture the state of the art in quantifying greenhouse gases from agricultural systems, with the goal of better understanding our current capabilities and near-term potential for improvement, with particular attention to quantification issues relevant to smallholders in developing countries. This work is timely in light of international discussions and negotiations around how agriculture should be included in efforts to reduce and adapt to climate change impacts, and considering that significant climate financing to developing countries in post-2012 agreements may be linked to their increased ability to identify and report GHG emissions (Murphy et al 2010, CCAFS 2011, FAO 2011).

Greenhouse Gases and Human Well-Being: China in a Global Perspective

Carbon pricing is about the explicit pricing of greenhouse gas (GHG) emissions, of which carbon dioxide is the most important. GHG emissions, which are normally measured in tonnes of carbon dioxide equivalent units, are responsible for global warming and hence the greatest environmental externality of our age. Carbon pricing is a mechanism for making society account for the external damage caused by carbon emissions in economic decision making. There are two main ways of pricing carbon dioxide emissions, either via a carbon tax or via the introduction of an emissions trading scheme whereby those emitting carbon into the atmosphere are required to surrender permits which reflect the quantity of emissions they are responsible for. These emission permits are tradeable and hence command a price and, in some respects, operate in a similar way to a carbon tax. Thus, we will discuss both carbon pricing and emissions trading, as the literature on both is closely related. Emissions trading exists for certain other pollutants (such as sulphur dioxide) and we will discuss some of the literature related to this. However, most of the literature on emissions trading relates to carbon dioxide emissions, as these are by far the most valuable traded emissions globally. The literature on carbon pricing and emissions trading is wide ranging and constantly being updated with new analyses. Much of the literature is written by economists who are seeking to apply market-based approaches to the solution of environmental problems. The article starts by looking at the general context in which carbon pricing and emissions trading sits before discussing introductory texts which relate to the subject and going on to introduce the relevant classic literature in environmental economics. It then proceeds to more applied literature, beginning with discussions of early examples of emissions trading and carbon taxation, before continuing to studies of the impact of carbon pricing and emissions trading and those which explain the nature of the schemes we observe. The article continues with literature which looks at the Europe Union Emissions Trading Scheme (EU ETS) for GHGs and other important carbon pricing schemes. It then moves on to the literature on the prospects for a global carbon price, on interactions with other climate policies, on distributional concerns about the imposition of a price on carbon. Finally, it concludes with an introduction to relevant official publications and sources of data on carbon emissions and carbon prices.

Abstract. To track progress towards keeping global warming well below 2 ∘C or even 1.5 ∘C, as agreed in the Paris Agreement, comprehensive up-to-date and reliable information on anthropogenic emissions and removals of greenhouse gas (GHG) emissions is required. Here we compile a new synthetic dataset on anthropogenic GHG emissions for 1970–2018 with a fast-track extension to 2019. Our dataset is global in coverage and includes CO2 emissions, CH4 emissions, N2O emissions, as well as those from fluorinated gases (F-gases: HFCs, PFCs, SF6, NF3) and provides country and sector details. We build this dataset from the version 6 release of the Emissions Database for Global Atmospheric Research (EDGAR v6) and three bookkeeping models for CO2 emissions from land use, land-use change, and forestry (LULUCF). We assess the uncertainties of global greenhouse gases at the 90 % confidence interval (5th–95th percentile range) by combining statistical analysis and comparisons of global emissions inventories and top-down atmospheric measurements with an expert judgement informed by the relevant scientific literature. We identify important data gaps for F-gas emissions. The agreement between our bottom-up inventory estimates and top-down atmospheric-based emissions estimates is relatively close for some F-gas species (∼ 10 % or less), but estimates can differ by an order of magnitude or more for others. Our aggregated F-gas estimate is about 10 % lower than top-down estimates in recent years. However, emissions from excluded F-gas species such as chlorofluorocarbons (CFCs) or hydrochlorofluorocarbons (HCFCs) are cumulatively larger than the sum of the reported species. Using global warming potential values with a 100-year time horizon from the Sixth Assessment Report by the Intergovernmental Panel on Climate Change (IPCC), global GHG emissions in 2018 amounted to 58 ± 6.1 GtCO2 eq. consisting of CO2 from fossil fuel combustion and industry (FFI) 38 ± 3.0 GtCO2, CO2-LULUCF 5.7 ± 4.0 GtCO2, CH4 10 ± 3.1 GtCO2 eq., N2O 2.6 ± 1.6 GtCO2 eq., and F-gases 1.3 ± 0.40 GtCO2 eq. Initial estimates suggest further growth of 1.3 GtCO2 eq. in GHG emissions to reach 59 ± 6.6 GtCO2 eq. by 2019. Our analysis of global trends in anthropogenic GHG emissions over the past 5 decades (1970–2018) highlights a pattern of varied but sustained emissions growth. There is high confidence that global anthropogenic GHG emissions have increased every decade, and emissions growth has been persistent across the different (groups of) gases. There is also high confidence that global anthropogenic GHG emissions levels were higher in 2009–2018 than in any previous decade and that GHG emissions levels grew throughout the most recent decade. While the average annual GHG emissions growth rate slowed between 2009 and 2018 (1.2 % yr−1) compared to 2000–2009 (2.4 % yr−1), the absolute increase in average annual GHG emissions by decade was never larger than between 2000–2009 and 2009–2018. Our analysis further reveals that there are no global sectors that show sustained reductions in GHG emissions. There are a number of countries that have reduced GHG emissions over the past decade, but these reductions are comparatively modest and outgrown by much larger emissions growth in some developing countries such as China, India, and Indonesia. There is a need to further develop independent, robust, and timely emissions estimates across all gases. As such, tracking progress in climate policy requires substantial investments in independent GHG emissions accounting and monitoring as well as in national and international statistical infrastructures. The data associated with this article (Minx et al., 2021) can be found at https://doi.org/10.5281/zenodo.5566761.

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This paper introduces a multi-period, multi-product green inventory routing problem with transshipment option, where capacitated vehicles distribute products from multiple suppliers to one customer to meet the given demand of products. The demand associated with the customer is assumed to be time-varying and deterministic. Greenhouse gas emissions from transport activities in a supply chain are a main reason for global warming. One of the main types of greenhouse gas is CO2 from vehicles and its impact on the environment. Inventory and routing decisions can help in the reduction of CO2 emissions if these emissions are taken into account by researchers. Also, as one of the main topics of this paper, the transshipment option is considered in the proposed model. The model is a mixed-integer programming (MIP) which has been solved and validated by General Algebraic Modeling System (GAMS). Finally, small and large-scale test problems are randomly generated and solved by the simulated annealing algorithm (SA). The computational results for different test problems showed that the proposed SA performs well and converges fast to reasonable solutions compared with GAMS. According to the results, it is determined that the transshipment option reduces CO2 emissions and costs by shortening the distance traveled.

The greenhouse gas (GHG) emissions of the global ceramic production is estimated at more than 400 Mt CO2/year, which have increased steadily from economic growth. Among ceramic industries, ceramic tableware industry (CTI) is a highly energy-intensive and high GHG emissions industry. Thailand was the fourth highest ranking ceramic tableware exporting country in the world. However, information on GHG emission from this industry was limited. This research aimed to investigate the carbon dioxide(CO2) intensity of CTI in Thailand and the annual projections of GHG emission during 2017–2050 with different GDP growths. Then, the energy saving potentials and GHG mitigation measures with their GHG abatement cost for small and large-scale CTI were proposed. The results indicated that the average CO2 intensity of Thailand CTI was 1.75 kg CO2e/kg of product. The projections for GHG emissions of ceramic tableware production with gross domestic production (GDP) growth rates of 1.5, 3.5 (BAU), and 5.5%, reached their maximum emissions at 220,500 t CO2 in 2029, 2022, and 2020, respectively. Under a BAU scenario, ceramic tableware production in 2022 would emit GHG at a rate approximately 1.37 times greater compared to the emissions in 2016. The maximum GHG reduction (100% implementation) was 48,902 t CO2e, accounting for 22% of GHG emissions in 2030. The average mitigation cost was 6.64 USD/t CO2e reduction. This study provided a guideline for the assessment of CO2 intensity and the technical information for long-term GHG emission projection in CTI which could be applied in worldwide.

The dairy Carbon Offset Scenario Tool (COST) was developed to explore the influence of various abatement strategies on greenhouse gas (GHG) emissions for Australian dairy farms. COST is a static spreadsheet-based tool that uses Australian GHG inventory methodologies, algorithms and emission factors to estimate carbon dioxide, methane and nitrous oxide emissions of a dairy farm system. One of the key differences between COST and other inventory-based dairy GHG emissions calculators is the ability to explore the effect of reducing total farm emissions on farm income, assuming the strategy was compliant with Kyoto rules for carbon offsets. COST provides ten abatement strategies across the four broad theme areas of diet manipulation, herd and breeding management, feedbase management and waste management. Each abatement strategy contains four sections; two sections for data entry (baseline farm data specific to the strategy explored and strategy-specific variables) and two sections for results (milk production results and GHG/economic-related results). Key sensitive variables for each strategy, identified from prior research, and prices for milk production and carbon offsets are adjusted through up/down buttons, which allows users to quickly explore the impact of these variables on farm emissions and profitability. For example, if the cost to implement an abatement strategy is doubled, what carbon offset income would be required to negate this additional cost? Results are presented as changes in carbon offset income, strategy implementation cost, additional milk production income and net farm income on a per annum and on a per GHG emissions intensity of milk production basis. COST currently contains a comprehensive range of strategies for GHG abatement, although some strategies are still in development. As new technologies or farm management practices leading to a reduction in GHG emission become available, these too will be incorporated into COST. To date, two dairy-specific abatement methodologies have been legislated as part of Australia’s commitment to reducing on-farm GHG emissions through it’s the carbon offset scheme, the Carbon Farming Initiative (CFI) and are incorporated into COST. These are the ‘Destruction of methane generated from dairy manure in covered anaerobic ponds’ and the ‘Methodology for reducing greenhouse gas emissions in milking cows through feeding dietary additives’. As an example, we explored the mitigation option Replace supplements with a source of dietary fats (reflecting the second above-mentioned CFI legislated abatement strategy) as feeding a diet higher in dietary fats has been shown to reduce enteric methane emissions per unit of feed intake. A 400 milking herd was fed a baseline diet of 2.6% dietary fat. By replacing grain with hominy meal, at a rate of 5.0 kg dry matter/ cow per day for 90 days during the 3 summer months, the summer diet fat concentration was increased to 6.4%. Enteric methane emissions were reduced by 40 tonnes of carbon dioxide equivalents (t CO 2 e) per annum for the farm. Waste methane and nitrous oxide emissions were also reduced by 0.5 and 1.6 t CO 2 e/annum, respectively. However, as reductions from these two sources of GHG emissions do not qualify for payment with this CFI methodology, their reduction could not be included as an offset income. At a carbon price of $20/ t CO 2 e, the reduction in enteric methane emissions was valued at $800/farm. The implementation cost of replacing grain with hominy was valued at $18,000/farm due to the hominy meal costing an additional $100/t dry matter compared to the grain. However, the additional milk production achieved due to the higher energy concentration of the diet resulted in an additional 70,200 litres and based on a summer milk price of $0.38/ litre, this equated to an additional income from milk valued at $26,676/farm. The overall result was a net increase in farm profit of $9,476/farm when paid on a reduction in total GHG emissions. COST can quickly allow users to ascertain the level of GHG emission reduction possible with various mitigation options and explore the sensitivity of key variables on GHG emissions and farm profitability.

Excessive emissions of certain trace gases such as carbon dioxide, methane, nitrous oxide, carbon monoxide and chlorofluorocarbons are likely to result in global warming due to increased concentration of these greenhouse gases (GHGs). Therefore, measures to control GHG emissions are essential and the current international debate is on how to arrive at optimal GHG limitation strategies. To set emission targets or to distribute the burden of costs of various control measures, it is necessary to identify the major emitters. The World Resources Institute has assessed countrywide contributions to global GHG emissions for 1987. This paper disagrees with the basic approach adopted by WRI because it fails to apportion sinks on a logical basis by keeping in mind equity considerations; it does not account for the residence time of different GHGs; and it uses unreliable and outdated data for estimating the emissions. Using recent and reliable data for India and Brazil as well as the IPCC global warming potential for various GHGs, the shares in global emissions have been recalculated. The paper concludes that if only current emissions are considered there is considerable bias against those countries which are latecomers to the process of industrialization.

Rivers play an important role in greenhouse gas emissions. Over the past decade, because of global urbanization trends, rapid land use changes have led to changes in river ecosystems that have had a stimulating effect on the greenhouse gas production and emissions. Presently, there is an urgent need for assessments of the greenhouse gas concentrations and emissions in watersheds. Therefore, this study was designed to evaluate river-based greenhouse gas emissions and their spatial-temporal features as well as possible impact factors in a rapidly urbanizing area. The specific objectives were to investigate how river greenhouse gas concentrations and emission fluxes are responding to urbanization in the Liangtan River, which is not only the largest sub-basin but also the most polluted one in Chongqing City. The thin layer diffusion model method was used to monitor year-round concentrations of pCO2, CH4, and N2O in September and December 2014, and March and June 2015. The pCO2 range was (23.38±34.89)-(1395.33±55.45) Pa, and the concentration ranges of CH4 and N2O were (65.09±28.09)-(6021.36±94.36) nmol·L-1 and (29.47±5.16)-(510.28±18.34) nmol·L-1, respectively. The emission fluxes of CO2, CH4, and N2O, which were calculated based on the method of wind speed model estimations, were -6.1-786.9, 0.31-27.62, and 0.06-1.08 mmol·(m2·d)-1, respectively. Moreover, the CO2 and CH4 emissions displayed significant spatial differences, and these were roughly consistent with the pollution load gradient. The greenhouse gas concentrations and fluxes of trunk streams increased and then decreased from upstream to downstream, and the highest value was detected at the middle reaches where the urbanization rate is higher than in other areas and the river is seriously polluted. As for branches, the greenhouse gas concentrations and fluxes increased significantly from the upstream agricultural areas to the downstream urban areas. The CO2 fluxes followed a seasonal pattern, with the highest CO2 emission values observed in autumn, then successively winter, summer, and spring. The CH4 fluxes were the highest in spring and the lowest in summer, while N2O flux seasonal patterns were not significant. Because of the high carbon and nitrogen loads in the basin, the CO2 products and emissions were not restricted by biogenic elements, but levels were found to be related to important biological metabolic factors such as the water temperature, pH, DO, and chlorophyll a. The carbon, nitrogen, and phosphorus content of the water combined with sewage input influenced the CH4 products and emissions. Meanwhile, N2O production and emissions were mainly found to be driven by urban sewage discharge with high N2O concentrations. Rapid urbanization accelerated greenhouse gas emissions from the urban rivers, so that in the urban reaches, CO2/CH4 fluxes were twice those of the non-urban reaches, and all over the basin N2O fluxes were at a high level. These findings illustrate how river basin urbanization can change aquatic environments and aggravate allochthonous pollution inputs such as carbon, nitrogen, and phosphorus, which in turn can dramatically stimulate river-based greenhouse gas production and emissions; meanwhile, spatial and temporal differences in greenhouse gas emissions in rivers can lead to the formation of emission hotspots.

This paper presents a detailed study concerning the importance of the planning horizon when solving inventory routing problems (IRPs). We evaluate the quality of decisions obtained by solving a finite‐horizon IRP. We also discuss the relevance of explicitly considering profit maximization models rather than the traditional cost minimization variant. As a means to this end, we describe four classes of the IRP corresponding to different types of markets. Two of them lead to nonlinear models, which are linearized. Furthermore, we provide a deterministic simulator to evaluate the long‐term effects arising from using planning horizons of varying lengths when solving the IRP. A computational study is performed on cases generated from benchmark data instances. The results confirm that the long‐term performance of the IRP decisions is, in part, contingent on the length of the selected planning horizon. They also show that considering profit maximization instead of cost minimization leads to different decisions, generating considerably more revenue and profits, albeit not nearly as much as suggested by individual solutions to static IRPs with short planning horizons.

Carbon emissions are one of the components of greenhouse gas (GHG) emissions in the form of carbon dioxide (CO2) which mainly comes from the use of fossil fuels and various human activities. Based on fossil fuel and industrial emissions data, in 2022 Indonesia ranked as the 6th largest carbon emitting country in the world after China, the United States, India, Russia and Japan, with 728.88 million tons of CO2e total emissions. The Ministry of Industry reported that total GHG emissions from Indonesia's industrial sector reached 238.1 million tons of CO2e in 2022. In the mining industry, GHG emissions originate from deforestation, energy use, processing, and refining, with non-renewable energy use being the largest emitter. GHG emissions from primary mineral and metal production contribute approximately 10% of global GHG emissions related to energy. Each mining industry may have varying primary sources of GHG emissions depending on the type of mine and the final product. Mining industries, as one of the major emitters, must conduct GHG inventories to understand emission sources and develop effective reduction strategies. GHG inventory activities are conducted based on guidelines provided by the Ministry of Environment and Forestry's National Greenhouse Gas Inventory Management Handbook (2012), Book II Volumes 1-4. Efforts to reduce GHG emissions can be undertaken through planting plants that have high carbon absorption and storage capabilities in mine reclamation activities, as well as using New and Renewable Energy (NRE) as an alternative to replace fossil energy.

It is well known that simulated annealing (SA) and genetic algorithm (GA) are two global methods and can then be used to determine the optimal solution of NP-hard problem. In this paper, due to difficulty of obtaining the optimal solution in medium and large-scaled problems, a hybrid genetic algorithm (HGA) was also developed. The proposed HGA incorporates simulated annealing into a basic genetic algorithm that enables the algorithm to perform genetic search over the subspace of local optima. The two proposed solution methods were compared on Rosenbrock function global optimal problems, and computational results suggest that the HGA algorithm have good ability of solving the problem and the performance of HGA is very promising because it is able to find an optimal or near-optimal solution for the test problems.

Based on the localized data of environmental load, this study has establishedthe life cycle assessment (LCA) model of battery electric passenger vehicle(BEPV) that be produced and used in China, and has evaluated the energyconsumption and greenhouse gases (GHGs) emission during vehicle pro-duction and operation. The results show that the total energy consumptionand GHG emissions are 438GJ and 37,100kg (in terms of CO2 equivalent)respectively. The share of GHG emissions in total emissions at the productionstage is 24.6%, and 75.4% GHG emissions are contributed by the operationalstage. The main source of energy consumption and GHG emissions at vehicleproduction stage is the extraction and processing of raw materials. TheGHG emissions of raw materials production accounts for 75.0% in the GHGemissions of vehicle production and 18.0% in the GHG emissions of fulllife cycle. The scenario analysis shows that the application of recyclablematerials, power grid GHG emission rates and vehicle energy consumption rates have significant influence on the carbon emissions in the life cycle ofvehicle. Replacing primary metals with recycled metals can reduce GHGemissions of vehicle production by about 7.3%, and total GHG emissionscan be reduced by about 1.8%. For every 1% decrease in GHG emissionsper unit of electricity, the GHG emissions of operation stage will decrease byabout 0.9%; for every 1.0% decrease in vehicle energy consumption rate, thetotal GHG emissions decrease by about 0.8%. Therefore, developing cleanenergy, reducing the proportion of coal power, optimizing the productionof raw materials and increasing the application of recyclable materials areeffective ways to improve the environmental performance of BEPV.