Increase In Cost Research Articles

Methodology for improving the assessment of the economic efficiency of transport infrastructure development

Transport infrastructure, especially railway infrastructure, plays a key role in economic development, ensuring efficient movement of goods and people. It is characterised by high capital intensity, long operational lifespans, and generation of socio-economic effects. Therefore, there is a need to improve the methodology for assessing the economic efficiency of creating and developing transport infrastructure objects. The traditional approach to evaluation is based on discounting cash flows. When applying this method, significant capital investments are compared with devalued effects in subsequent years due to discounting, during which the infrastructure object reaches the specified estimated load. Moreover, it does not consider the fact that the transport infrastructure benefits and holds value not only for the current generation but also for the future one. Additionally, it, as an economic good, cannot be seen as a one-timely consumed product: this transportation service cannot be accumulated. For a more accurate and comprehensive assessment of the efficiency, particularly in the case of railway transport, it is proposed to consider the results of long-term operation of the transport infrastructure at full (estimated) load not in the future but in the present (present сontinious). The economic assessment indicators include initially invested and adjusted capital, annual effect, efficiency ratio, and the value of the transport infrastructure object. The research results aim to provide an adequate assessment of project implementation efficiency in the field in question. In the future, the methodology can be enhanced by defining risks of not ensuring normal infrastructure load throughout the entire calculation period as well as by assessing the reduction in profitability or increase in transportation costs.

The gravity of distance: evidence from a trade embargo

Abstract On 5 June 2017, an airspace blockade was imposed on the State of Qatar by Bahrain, Saudi Arabia, United Arab Emirates (neighboring countries), and Egypt. We exploit this exogenous increase in air transportation costs toward non-blockading countries to examine the effect of increased travel distance, due to re-routing, on bilateral trade. Based on a gravity model estimated using a Poisson pseudo-maximum likelihood estimator, we find a distance elasticity of imports between −0.3 and −0.5. Overcoming the limitations of cross-sectional studies and taking advantage of this quasi-natural experiment, our findings are robust and revise downward previous estimates of the distance elasticity.

Comparative time series analysis of Anatolian water buffalo stock in Türkiye

Water buffalo holds an important place in the livestock heritage in Türkiye. In the current study, buffalo numbers between 2023 and 2032 were estimated by using buffalo number data from 1991 to 2022. In the study, FAOSTAT data between 1961 and 2021 and TÜİK data for 2022 were used to obtain the time series. ARIMA modeling method was used in the study conducted in R programming language. ARIMA is a time series model that can make predictions from time series based on explaining univariate data. According to the results of the analysis made in the study, it is seen that the number of buffaloes in Türkiye will continue to decrease. According to the results obtained, it is estimated that the number of buffaloes will be 157,755 heads in 2023, 96,851 heads in 2027 and 19,136 heads in 2032. The disease resistance of buffaloes, their high adaptability, the high demand for water buffalo products, the preference for high-yielding cows for milk production despite government support payments, increasing mechanization, the reduction of buffalo habitats and the increase in input costs have led to a decline in Anatolian water buffalo populations. To prevent the Anatolian water buffalo from being threatened with extinction, the population decline must be halted and improved. To ensure this, the decline of the pastures that form the buffalo’s habitat must be halted, pasture areas must be increased, and existing pastures rehabilitated, government subsidies for buffalo and calf rearing must be increased and market opportunities for buffalo products must be expanded.

Modeling a Time Study of the United States Consumer Price Index from 2014 to 2023

This study presents a comprehensive analysis of the U.S. Consumer Price Index (CPI) over a ten-year period 2014 to 2023. The CPI, a critical measure of inflation, reflects the average change in prices paid by urban consumers for a market basket of goods and services. This study will explore the intricate relationships between CPI and key economic variables, including unemployment levels, interest rates, and the sales index, using time series data and multiple regression analysis. The research identifies significant predictors of CPI and evaluates the extent to which these factors influence price levels in the U.S. economy. The findings reveal a strong positive correlation between CPI and interest rates, indicating that as borrowing costs increase, the CPI tends to rise correspondingly. Conversely, a negative relationship is observed between CPI and unemployment levels, suggesting that higher unemployment rates are associated with a decrease in CPI, possibly due to reduced consumer spending. The study also examines the sales index, finding a weaker yet notable relationship with CPI, which underscores the complex dynamics of consumer behavior and price levels. The analysis highlights the limitations of CPI as a comprehensive measure of the cost of living, pointing out its lack of significant correlation with real income and population growth. The study concludes that while CPI is an essential tool for measuring inflation and informing monetary policy, it may not fully capture the economic realities faced by the population. These insights suggest the need for policymakers to consider additional factors when using CPI to make informed decisions about economic policy and social welfare programs among the U.S. population.

Inventory classification with artificial intelligence: Conceptual framework

In today's world where competition is increasing harshly, it is important to achieve sustainable profits and keep costs competitive. Under these conditions, the importance of supply chain elements is increasing day by day. Management of inventory costs, which constitute a large volume among cost items, affects the performance of companies. The first step to focus on to keep inventory costs under control and improve is inventory classification. Inventory classification, which is at the top of the supply chain elements and closely affects the subsequent phases, it is critical in determining supply chain performance. Thanks to inventory classification, material groups are determined and stock strategies for these groups are clarified. Incorrect inventory classification causes materials to be assigned to incorrect groups, which negatively affects inventory costs and subsequent phases of the supply chain, causing an increase in costs. The most used methods for inventory classification are ABC analysis, multi-criteria inventory classification and optimization. However, the increasing momentum in artificial intelligence studies in recent years has also closely affected inventory classification. The advantages brought by artificial intelligence methods have also created distinctive contributions to inventory classification studies. This study provides a conceptual framework that examines artificial intelligence methods in the field of inventory classification.

Sealing Characteristics Analysis of New Subsea Wellhead Sealing Device

Subsea wellhead systems are the crucial equipment for the development of oil and gas resources offshore, while the sealing device plays a vital role as the main component of the wellhead system. Once the seal fails, it is necessary to retrieve the original wellhead system and either repair the sealing device or reinstall a new one. This will result in a delay in normal production and an increase in development costs. Therefore, a novel subsea wellhead sealing device is designed. A finite element analysis model is developed to study the underwater wellhead sealing mechanism regarding the equivalent stress and contact stress. The research results show that as the driving block gradually increases from 4 mm to 12 mm, the stress of the 12 convex parts on the sealing body also increases. The maximum equivalent stress reaches 3.5 times the yield limit, indicating that it has entered the yield stage and can achieve a more effective seal. The analysis of the contact stress of the sealing body reveals that the contact stress of the driving block increases, leading to plastic deformation of the sealing body while driving it to achieve a complete seal. In general, the finite element simulation results are consistent with the engineering practice. By analyzing the sealing characteristics, it can serve as the foundation for designing and providing theoretical support for the optimization of the metal-sealing structure.

Identifying Robust Decarbonization Pathways for the Western U.S. Electric Power System Under Deep Climate Uncertainty

AbstractClimate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

A Retrospective Assessment of Guideline Adherence and Treatment Outcomes From Clostridioides difficile Infection Following the IDSA 2021 Clinical Guideline Update: Clostridioides difficile Infection

Abstract Background The 2021 update to the Infectious Diseases Society of America Clostridioides difficile infection (CDI) guidelines recommended fidaxomicin as the preferred treatment over vancomycin for patients with initial and recurrent CDI. Few studies have examined how treatment patterns and clinical outcomes of hospitalized CDI patients changed after the postguideline update or contemporary real-world outcomes of fidaxomicin vs vancomycin. Methods This retrospective, observational study used the PINC AI Healthcare Database on adult patients who received CDI treatment between 1/2020 and 6/2021 (pre period) and between 10/2021 and 9/2022 (post period). We examined treatment patterns of fidaxomicin, vancomycin, and metronidazole, as well as clinical and health care resource use outcomes of patients treated exclusively with fidaxomicin vs vancomycin, using nearest-neighbor propensity matching and hierarchical regression methods. As a sensitivity analysis, we repeated the fidaxomicin vs vancomycin comparisons among patients with recurrent and nonrecurrent index infections. Results A total of 45 049 patients with CDI from 779 US hospitals met initial inclusion criteria. Comparing the pre vs post periods, the proportion of patients treated with fidaxomicin increased from 5.9% to 13.7% (P < .001), vancomycin use decreased from 87.9% to 82.9% (P < .001), and metronidazole use decreased from 21.6% to 17.2% (P < .001). When comparing fidaxomicin vs vancomycin in the post period, fidaxomicin was associated with lower CDI recurrence (6.1% vs 10.2%; P < .001) and higher sustained clinical response (91.7% vs 87.8%; P < .001). Ninety-day postdischarge costs were not significantly different between groups. A sensitivity analyses showed similar findings. Conclusions Since the 2021 guideline update, fidaxomicin use has increased significantly but could be further utilized given its association with better clinical outcomes and no increase in postdischarge costs.

Carbon footprint reduction in an e-commerce market: impacts of logistics service sharing

In the e-commerce market, green technology investment and carbon offset purchase are two widely-used approaches for reducing the carbon footprint of logistics services. However, implementing carbon footprint reduction in logistics services generally incurs heavy costs. To distribute heavy costs and improve resource utilization, a novel strategy of logistics service sharing has played an important role. Nevertheless, it is still unclear, in a co-opetitive e-commerce market, how to make use of different approaches and what the impacts of logistics service sharing are. To answer these questions, we develop an analytical model where an e-commerce platform and a third-party seller compete for consumers who care about both the selling price and the logistics service carbon footprint. We assume that the platform builds his own logistics service system and can adopt two different approaches to cut carbon footprint, while the seller needs to outsource logistics services to the TPL service provider or the platform. The analysis shows that without logistics service sharing, the platform may be more willing to purchase carbon offset as the offset cost increases, and the availability of carbon offset can prompt the platform to increase his investment in green technology. By contrast, with logistics service sharing, the platform will be less willing to purchase carbon offset as the offset cost increases, and the usage of carbon offset will induce him to invest less in green technology. These findings highlight that logistics service sharing can change the platform's preference toward the two approaches and modify the relationship between them. Furthermore, we find that logistic service sharing can provide a win-win outcome for the platform and the seller, but is not necessarily helpful for reducing the total carbon footprint in the e-commerce supply chain as well as increasing consumer surplus and social welfare.

Constrained multi-scale dense connections for biomedical image segmentation

Multi-scale dense connection has been widely used in the biomedical image community to enhance the segmentation performance. In this way, features from all or most scales are aggregated or iteratively fused. However, by analyzing the details, we discover that some connections involving distant scales may not contribute to, or even harm, the performance, while they always introduce a noticeable increase in computational cost. In this paper, we propose constrained multi-scale dense connections (CMDC) for biomedical image segmentation. In contrast to current general lightweight approaches, we first introduce two methods, a naive method and a network architecture search (NAS)-based method, to remove redundant connections and verify the optimal connection configuration, thereby improving overall efficiency and accuracy. The results demonstrate that the two approaches obtain a similar optimal configuration in which most features at the adjacent scales are connected. Then, we applied the optimal configuration to various backbone networks to build constrained multi-scale dense networks (CMD-Net). Experimental results evaluated on eight image segmentation datasets covering biomedical images and natural images demonstrate the effectiveness of CMD-Net across a variety of backbone networks (FCN, U-Net, DeepLabV3, SegNet, FCNsa, ConvUNeXt) with a much lower increase in computational cost. Furthermore, CMD-Net achieves state-of-the-art performance on four publicly available datasets. We believe that the CMDC method can offer valuable insight for ways to engage in dense connectivity at multiple scales within communities. The source code has been made available at https://github.com/JerRuy/CMD-Net.

Glycerol as a Feedstock for Chemical Synthesis

AbstractGlycerol, defined simply as a colorless, sweet syrupy liquid extracted from fatty substances through saponification, is an alcohol with three hydroxyl (OH–) groups in its structure. Glycerol has many uses in the consumer market. It is used primarily in personal care products, as an adhesive and sealing agent and many applications. Glycerol, whose name is propane‐1,2,3‐triol, standardized by the International Union of Pure and Applied Chemistry (IUPAC), CHO open formula CH2OH–CHOH–CH2OH. It can be said that glycerol, a by‐product of biodiesel, is produced in very high quantities. Retention of the produced glycerol will lead to cost increases and environmental problems that may directly affect the development of the biodiesel market. Due to the supply of glycerol to the market in large quantities, glycerol prices have hit the bottom, and therefore, the income and profitability of biodiesel production factories from the sale of glycerol have decreased. This situation clearly shows that the excess of glycerol now poses an obstacle to developing the biodiesel market. This article aims to list the valuable chemicals into which glycerol, produced in large quantities as a biodiesel by‐product, can be converted under a single heading and to detail the studies carried out on this subject.

Optimizing the economic dispatch of weakly-connected mini-grids under uncertainty using joint chance constraints

AbstractIn this paper, we deal with a renewable-powered mini-grid, connected to an unreliable main grid, in a Joint Chance Constrained (JCC) programming setting. In several rural areas in Africa with low energy access rates, grid-connected mini-grid system operators contend with four different types of uncertainties: forecasting errors of solar power and load; frequency and outages duration from the main-grid. These uncertainties pose new challenges to the classical power system’s operation tasks. Three alternatives to the JCC problem are presented. In particular, we present an Individual Chance Constraint (ICC), Expected-Value Model (EVM) and a so called regular model that ignores outages and forecasting uncertainties. The JCC model has the capability to guarantee a high probability of meeting the local demand throughout an outage event by keeping appropriate reserves for Diesel generation and battery discharge. In contrast, the easier to handle ICC model guarantees such probability only individually for different time steps, resulting in a much less robust dispatch. The even simpler EVM focuses solely on average values of random variables. We illustrate the four models through a comparison of outcomes attained from a real mini-grid in Lake Victoria, Tanzania. The results show the dispatch modifications for battery and Diesel reserve planning, with the JCC model providing the most robust results, albeit with a small increase in costs.

The rising cost of liver transplantation in the United States.

Liver transplantation (LT) is the only curative treatment for end-stage liver disease and significantly improves patient outcomes. However, LT is resource-intensive and costly, with expenditures rising dramatically in recent years. Factors contributing to this increase in cost include expanded transplant criteria, utilization of marginal organs, and broader organ distribution, resulting in significant logistical expenses. Advanced technologies like organ perfusion devices, while promising better outcomes, further inflate costs due to their high price and market monopolization. Moreover, living donor liver transplant (LDLT) and utilization of donation after cardiac death (DCD) organs introduce higher initial expenditures yet potential long-term savings. Despite rising costs, reimbursement has remained largely stagnant, putting financial strain on transplant programs, and threatening their sustainability. This review examines the multifaceted drivers of rising costs in LT, focusing on recent policy changes, the role of organ procurement organizations (OPOs) and the impact of new technologies. We also propose comprehensive solutions at national, OPO, and local levels, including optimizing resource allocation, leveraging regional collaborations, and advocating for revised reimbursement models to curb escalating costs. Addressing these challenges is critical to ensuring the continued viability of LT programs and maintaining patient access to this life-saving intervention.

Changes in the Epidemiological Landscape of Diabetes in South Korea: Trends in Prevalence, Incidence, and Healthcare Expenditures.

Diabetes is a serious public health concern that significantly contributes to the global burden of disease. In Korea, the prevalence of diabetes is 12.5% among individuals aged 19 and older, and 14.8% among individuals aged 30 and older as of 2022. The total number of people with diabetes among those aged 19 and older is estimated to be 5.4 million. The incidence of diabetes decreased from 8.1 per 1,000 persons in 2006 to 6.3 per 1,000 persons in 2014, before rising again to 7.5 per 1,000 persons in 2019. Meanwhile, the incidence of type 1 diabetes increased significantly, from 1.1 per 100,000 persons in 1995 to 4.8 per 100,000 persons in 2016, with the prevalence reaching 41.0 per 100,000 persons in 2017. Additionally, the prevalence of gestational diabetes saw a substantial rise from 4.1% in 2007 to 22.3% in 2023. These changes have resulted in increases in the total medical costs for diabetes, covering both outpatient and inpatient services. Therefore, effective diabetes prevention strategies are urgently needed.

Influence of Different Cow Manure Application Rates on the Growth, Yield and Indigo Blue Color of Indigofera suffruticosa Mill

Background: Indigo is a key ingredient in the natural dyeing of fabrics. However, the conventional use of chemical fertilizers in indigo cultivation has led to an increase in production costs and soil degradation. Low-fertility soils further compromise the growth and yield of indigo plants. To address these challenges, employing manure is recognized as a sustainable approach for indigo cultivation. The use of manure enhances soil nutrient levels, improves soil structure and mitigates production costs. In this study, our objectives were to compare different rates of cow manure in relation to indigo growth, yield and the effect of cow manure rate on the value of indigo blue color. Methods: The experiments were carried out at the Plant Science Field from June to October 2022. Employing a randomized complete block design (RCBD), the study featured four treatments with four replications each. The treatments included: Treatment 1: no fertilizer (control); Treatment 2: cow manure at a rate of 3,125 kg/ha; Treatment 3: cow manure at a rate of 6,250 kg/ha; and Treatment 4: cow manure at a rate of 9,375 kg/ha. Data collection included plant height, canopy width, number of branches, plant weight, indigo blue weight and indigo blue color value. The CIE color system (Commission International de l’Éclairage) determined the color value. Result: The application of cow manure at a rate of 9,375 kg/ha resulted in the highest plant height and canopy width, with the corresponding number of branches of 150.73 cm, 46.45 cm and 31.73 cm, respectively. As compared to the other treatments, these values were statistically different. The color that was associated with the indigo finished product was dark blue and the yield; plant weight and indigo blue weight were recorded as 15,663.44 kg/ha and 2,540.44 kg/ha, respectively. The color value of indigo blue showed that the L* values of 24.94, a* -9.32, b* -2.44 and C* 9.63 were not significant but h* 194.66 was statistically significant.

Cost-effectiveness analysis of direct oral anticoagulants versus low-molecular-weight heparin and no thromboprophylaxis in primary prevention of cancer-associated venous thromboembolism in China.

Cancer-associated venous thromboembolism (CAVTE) is a preventable, life-threatening complication with a considerable morbidity and mortality. Primary venous thromboembolism (VTE) prophylaxis is currently recommended; however, the health and economic benefits have not been evaluated and compared in China. This study aimed to assess and compare the cost-effectiveness of anticoagulants in primary CAVTE prevention among cancer patients in China. A Markov model with a 5-year horizon was established to evaluate the costs and effectiveness of direct oral anticoagulants (DOACs) compared to low-molecular-weight heparins (LMWHs) and no prevention in primary prophylaxis of CAVTE in China. Key clinical outcomes were obtained from the available clinical trials, comparing DOACs (rivaroxaban and apixaban) with LMWHs or with no thromboprophylaxis. Utility and the cost inputs were all obtained from the published literature or local data with public sources. The total costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs) were estimated as the main endpoints of the modal for each strategy. The assessment of uncertainty was performed involving deterministic sensitivity analysis and probabilistic sensitivity analysis (PSA). Impact of time horizon, generic drug price, and individual DOACs were assessed in scenario and subgroup analyses. Primary prophylaxis using DOACs were projected to yield 1.866 QALYs at a cost of $3,287.893, resulting in the ICERs of $12,895.851 (DOACs vs. no-thromboprophylaxis) and $43,613.184/QALYs (LMWHs vs. DOACs). Sensitivity analysis revealed that ICER was sensitive to the VTE and bleeding risk, drug cost of anticoagulants, self-payment ratio, and overall death rate of cancer. Probabilistic sensitivity analysis showed that DOACs and LMWHs had a 48% and 45% probability of being cost-effective at a 5-year time horizon, respectively. When the time horizon extended to 10 years, DOACs achieved a cost-effective probability of 43%. Among individual DOACs, apixaban was found to be the preferred strategy in VTE prevention due to its incremental health gain with an acceptable cost increase. Primary thromboprophylaxis with DOACs was cost-effective in cancer patients at a willing-to-pay (WTP) threshold of $37,125.24/QALY in China. Cancer death rate, risk of VTE and major bleeding, and the drug cost assumed greater relevance and importance in the decision-making process for primary thromboprophylaxis in cancer.

Possibilities of Changes in Energy Intensity of Production Depending on the Scale of Farm Investments in a Polish Region

The purpose of this paper was to analyze the possibility of changes in energy intensity of production in the context of farm investment scale. The empirical section relies on unpublished FADN microdata. The study answers the question of whether investments and fixed capital growth can contribute to improvements in environmental performance of agricultural production. As it turns out, the group of farms with the greatest amount of investments saw an increase in energy consumption costs, though at a much slower rate than growth in production value. As a result, there was a drop in energy intensity of production, defined as the ratio between energy costs and production value. These findings corroborate the hypothesis advanced in this study, namely that upon reaching a sufficiently large amount of investments, farms can become capable of reducing the energy intensity of their production activity. Hence, higher levels of capital productivity are attained when the farms invest in more energy-efficient fixed assets. Conclusions cannot be universal, as the analysis involved a portion of selected farms. However, the research indicates a trend in the study of energy intensity of production depending on the scale of investment and the validity of the investment comprehensiveness approach. These conclusions provide a basis for recommendations for an agricultural policy which should include support for investments that are large and comprehensive (in relation to the farm’s assets) and go beyond the simple renewal of assets. Therefore, in its investment-oriented measures, the agricultural policy should take the innovativeness (including energy efficiency) criterion into account.

Economic and environmental analysis of EVs' in urban transportation using system dynamics

The higher initial cost of EVs compared to gasoline-powered vehicles can hinder their widespread adoption in various markets. This study aims to evaluate the factors influencing EV adoption or rejection in a developing nation by examining the economic and environmental implications of integrating EVs into Tehran's transportation system. Using a system dynamics approach, the research incorporated a range of elements, including private and public transport, fuel consumption, and environmental costs. Based on this model, causal loop and stock-flow diagrams were developed. Model simulation was conducted, and its validity was carefully controlled. Different scenarios were then generated for incorporating EVs into Tehran's transportation system. The simulations demonstrated that integrating EVs into the taxi fleet is economically advantageous as it offsets the annual purchase and charging station expenses. This scenario led to a 0.2 % decrease in urban costs. Conversely, introducing EVs for private transportation resulted in a 1.9 % increase in overall transportation system costs. Therefore, for cities like Tehran, prioritizing EV adoption in the taxi fleet is a more favorable strategy to balance economic and environmental benefits. This model provides valuable insights for developing countries considering EV adoption, highlighting the economic and environmental ramifications across various sectors.

Analysis of the Development of Downstream Copper Industry in Indonesia to Obtain Maximum Added Value Using Dynamic Systems

This research aims to design a dynamic system model for the downstream copper processing industry. The secondary data obtained is data provided by the BKPM Strategic Investment Downstreaming Expert Team. Vensim software is used to simulate downstream development in the form of CLD and SFD from 2024 to 2045. The results of the value-added simulation resulted in an increase in the added value of copper cathode by 2.15 times, the added value of cathode slab by 1.62 times, the added value of copper billet by 1.65 times, the added value of copper bar &rods by 1.08 times, the added value of copper tube by 1.02 times, the added value of copper strip by 1.39 times, and the added value of copper wire by 1.58 times. After a sensitivity analysis of the selling price at CAGR and an increase in energy costs by 20%, there was a change in the average value-added margin, so that the increase in added value changed which resulted in an added value of copper cathode of 1.9 times, added value of cathode slab of 1.47 times, added value of copper billet of 1.49 times, added value of copper bar &rods of 1.26 times, added value of copper tube of 0.93 times, added value of copper strip of 0.98 times, and added value of copper wire of 1.44 times.

Cold Chain Distribution Route Optimization for Mixed Vehicle Types of Fresh Agricultural Products Considering Carbon Emissions: A Study Based on a Survey in China

With the improvement of people’s living standards and the widening of circulation channels, the demand for fresh agricultural products continues to increase. The increase in demand will lead to an increase in delivery vehicles, costs, and carbon emissions, among which the increase in carbon emissions will aggravate pollution and is not conducive to sustainable development. Therefore, it is very important to balance economic and environmental benefits in the distribution of fresh agricultural products. Based on the analysis of the distribution characteristics of fresh agricultural products, this paper studies the optimization of the cold chain distribution route of fresh agricultural products considering carbon emission. Firstly, the cold chain distribution route planning of fresh agricultural products was investigated and analyzed by the interview method, and the basis for establishing the model objective and constraint conditions was obtained. Then, taking the minimum total cost including carbon emission cost as the optimization goal, the cold chain distribution route optimization model for mixed vehicle types is established considering electric refrigerated vehicles, gasoline refrigerated vehicles, and so on. Genetic algorithm was used to solve the model, and MATLAB2018b was used to substitute specific case data for simulation analysis. The analysis results show that increasing the consideration of carbon emission and mixed vehicle types in the distribution route of fresh agricultural products can not only reduce the distribution cost but also reduce the carbon emission. To some extent, the research content of this paper can provide a reference for enterprises in planning cold chain distribution routes of fresh agricultural products.