Reduction Of Environmental Emissions Research Articles

Extending the Service Life of Existing Concrete Structures to Last Beyond 100 Years

New bridges and other concrete structures are being built using the design concepts presented in SHRP2 R19A (Bridges for Service Life Beyond 100 Years: Innovative Systems, Subsystems, and Components) and FIB Bulletin 34 (Model Code for Service Life Design) to achieve 100+ year service life. Existing concrete structures may not have been built with a specific design service life in mind but the principles of SHRP2 R19A can be used to maintain and extend the service life of existing concrete structures. Application of these principles and methods can extend service life by mitigating corrosion of embedded reinforcing steel by over 80%. Extending the service life of existing reinforced concrete structures provides many benefits to structure owners and society. Direct benefits include cost savings for owners and a general reduction in disruption to the public caused by repair and replacement. Extending service life also provides many indirect benefits including; reduction in the use of materials, reduction in the generation of demolition waste, reduction of environmental emissions, and the protection of sensitive habitat and existing ecosystems. This paper presents case studies which illustrate how the SHRP2 R19A protocol was used to design durable repairs to extend service life of existing concrete structures and the direct and indirect benefits which were achieved. Service life extension of existing reinforced concrete structures is a sustainable practice and should be encouraged.

520 Late-Breaking: Effect of Technology on Environmental Emissions from Four Representative U.S. Feedlots and the Beef Production System

Abstract Our objective was to evaluate effects of technological management strategies on environmental impacts and net returns of feedlot operations in the United States. Feedlot operations were simulated with the Integrated Farm System Model (IFSM 4.6; USDA-ARS, University Park, PA) to quantify baseline environmental impacts of feedlot production and full US beef cattle production systems. Strategies simulated included: ionophore, implant, ractopamine hydrochloride, combined management (ionophore, implant, and ractopamine hydrochloride; I+I+R), lubabegron, reduced mortality rate, and improved fiber digestion. Days on feed were adjusted whenever necessary and according to production practices typical of commercial feedlots. Subsequently, annual number of cattle finished by the operation was adjusted according to days on feed to maintain consistent one-time capacities. Mitigation strategies were individually modeled and simulated in IFSM for each feedlot operation to calculate intensities (expressed per kg gain) for greenhouse gas (GHG) emissions, fossil energy use, blue water consumption, and reactive nitrogen loss. Additionally, net returns to management were estimated for each feedlot operation. Feedlots were then integrated with simulations of cow-calf, stocker, and backgrounding operations to estimate environmental intensities (expressed per kg carcass weight) for the full beef cattle production system. Carbon emission intensity was reduced most using the I+I+R strategy (10%), followed by implant (6%) and ionophore (5%) strategies alone. Similarly, energy use intensity was reduced the greatest by I+I+R (9%), ionophore (5%), and implants (4%). Reductions in water use intensity were also greatest using I+I+R (9%). Net returns increased for all strategies compared to baseline net return with the greatest improvements observed for I+I+R ($114/finished animal) and implants ($66/animal). Consistent results were observed for all strategies simulated across all four environmental intensities when quantified for the full cattle production system. Implementing I+I+R (7%), ractopamine hydrochloride (4%), and lubabegron (4%) in feedlots resulted in the greatest reductions in environmental emissions.

Subsidised water symbiosis of eco-industrial parks: A multi-stage game theory approach

Designing water symbiosis networks in an industrial site is aimed to solve the water quality and security problem by minimising freshwater consumption or pollutant discharge. However, implementing the symbiosis requires an expensive capital cost on the site and may need cost compensation by the authority to facilitate the operation. This work aims to identify the distribution of stable and fair profits to the plants from the government to each stakeholder with the Cooperative Game approach. This study considers the grand coalition of finite players (industrial plants/stakeholders) with authority to facilitate water recycling in an eco-industrial park. The first stage includes the determination of the park authority's objectives (i.e., to minimise cost, resource usage, or pollutants discharge) if the stakeholders cooperate. In the next stage, the park authority can then compensate the cost by providing incentives or subsidies for the stakeholders that participate in the symbiosis. The incentives can be a rewarding scheme for the recycling efforts, while the subsidies are the money required to build the facilities. The stakeholders are then allowed to decide the recycling amount that maximises their economic interests. A wastewater tax can be imposed by the authority to the stakeholders to stimulate them to take part in the symbiosis while generating the money source for subsidisation. Proper game analysis is provided to analyze the Nash Equilibrium solutions of the tax rate. A hypothetical case study is used to demonstrate how fair subsidisation can be identified and the implementation of taxation policy. The proposed framework demonstrates the potential benefits and guidelines for each industrial plant, as well as the government, to derive a feasible cooperative policy to achieve the goal of environmental emissions reduction and can be applied to analyze similar symbiosis.

Agriculture and Environment Sustainability through Interaction of Microbiology with Soil Chemical Nature

Soil is an unsolidified entity in which microbes are diverse, having mutualistic, antagonistic, synergistic relationships with plants and provides a base for living. The synthetic inputs (fertilizers and pesticides) and anthropogenic practices aimed at agricultural production dramatically enhance the soil chemical reactions. Inorganic chemical reactions that occur in soil pollute the environment after entering into four major environmental compartments including water and air. Presence of radioactive gases in the atmosphere may cause significant changes in the earth's environment, including changes in precipitation and temperature along with increase in the regional and global runoff that causes ecosystem degradation and human health related issues through acid rain. It is a burning topic in today’s context since it is vital to conserve the ecosystem in a sustainable manner and as a result it decides whether global food production is increasing or decreasing. Management of these chemical processes by different methods is essential which could be a viable choice for the reduction of environmental emissions and improving growth and yield attributes of agricultural commodities. Among all the strategies, microbial adaptation in synthesizing reactions is crucial as it reduces ecosystem effects and increases the global food production for the growing population.

Environmental emissions reduction of a preoperative evaluation center utilizing telehealth screening and standardized preoperative testing guidelines

The healthcare sector is responsible for 8.5% of greenhouse gas (GHG) emissions in the United States. To prevent catastrophic effects of climate change, urgent reductions in GHG emissions are needed. Unnecessary preoperative testing and clinic visits contribute to excessive utilization of healthcare resources and patient travel, and addressing them may help reduce emissions. A retrospective cross-sectional analysis of GHG emissions associated with pre- and post- implementation of a novel telehealth preoperative evaluation process was performed on 298 American Society of Anesthesiologists Class I-III patients undergoing elective spine surgery at Yale New Haven Hospital. Electronic health records were evaluated to determine patient health status and necessary or unnecessary preoperative tests and clinic visits. Life cycle assessment and U.S. EPA GHG calculator were used to estimate carbon dioxide equivalent (CO2e) emissions from preoperative testing, clinic visits, and patient commuting. GHG emissions were reduced by 8.09 kg CO2e (9.6%) per patient, from 84.52 kg CO2e pre-intervention to 76.43 kg CO2e post-intervention (p = 0.019). The largest reduction, 14.71 kg CO2e, stemmed from fewer in-person clinic evaluations. An additional reduction of 18.07 kg CO2e per patient would have been possible if clinic performance were optimized (only necessary testing performed), resulting in a total reduction of 26.16 kg CO2e (31%). A preoperative clinic using established testing guidelines and telehealth can reduce environmental emissions. With more than 40 million annual surgeries nationwide, concerted effort to reduce unnecessary testing and visits should help curtail healthcare GHG emissions.

A big data mining approach for environmental emissions prediction of die casting process

As the key component of the manufacturing industry, die casting process suffers severe emission problems, which disobeys the prospects of Industry 4.0 and green manufacturing. Environmental emissions prediction of the die casting process is the foundation to optimize the environmental impact factors to achieve lower environmental pollution and health hazards of workers. However, the physical causal relationships between influential factors and environmental emissions are hard to be modeled. As a result, the environmental emissions prediction of the die casting process still faces a huge challenge. The big data mining approach can obtain the relationships disregarding the physical causal effect. This study firstly analyzes and demarcates nine impact factors that influence the four major environmental emissions PM2.5, PM10, noise, and temperature in the die casting process. Then, a big data mining approach integrating principal component analysis, particle swarm optimization, and back-propagation neural network is proposed for environmental emissions prediction. The effectiveness of the proposed approach is verified through a case study of die casting islands. The results show that the accuracy of the proposed method for the prediction of four environmental emissions and six types of die casting islands exceeds 90%. This proposed approach can strongly support the optimization of process parameters, layout of die casting island, etc. toward environmental emission reduction. It can also be easily applied to other processes such as plastic molding, machining, and extruding for emissions projection.

Investor rewards to environmental responsibility: Evidence from the COVID-19 crisis

The COVID-19 shock and its unprecedented financial consequences have brought about vast uncertainty concerning the future of climate actions. We study the cross-section of stock returns during the COVID-19 shock to explore investors' views and expectations about environmental issues. The results show that firms with responsible strategies on environmental issues experience better stock returns. This effect is mainly driven by initiatives addressing climate change (e.g., reduction of environmental emissions and energy use), is more pronounced for firms with greater ownership by investors with long-term orientation and is not observed prior to the COVID-19 crisis. Overall, the results indicate that the COVID-19 shock has not distracted investors' attention away from environmental issues but on the contrary led them to reward climate responsibility to a larger extent.

Marine environmental emission reduction policy in the liner shipping the economic impact from trade lane perspective

ABSTRACT This study focuses on the economic impact of the IMO Sulfur air pollution marine emissions reduction policy on carriers, as well as on socio-economic factors in the field of international liner shipping. Air pollution regulations, have the potential to shift freight away from its original port destination. This policy, which is implemented in an unequal manner (selective, global cap 0.5% out ECA and 0.1 in ECA), will create anew market failure from an economic and environmental perspective (‘pollution leakage’). This study will evaluate the economic impact by developing a Trade Lane (TL) Sulfur Emission Control Area (SECA) Cost Benefit Analysis (CBA) framework, based on the carrier problem, choosing an appropriate compliance action from aselection of alternatives, differentiated by compliance techniques. Results indicate that the scrubber is the most mature technological solution today, nevertheless, the expected impact on slot costs cannot be overlooked (compare to fuel switch alternative). Results indicate that a hypothetical alternative with a global sulfur content of 0.1% in a200 NM shore area and HFO uses in high seas were found to be more economic and less destructive to the maritime industry and less harmful to society in terms of health and potential pollution.

An evaluation framework of automated electric transportation system

Automated Electric Transportation (AET) is an innovative concept that aims to integrate energy, vehicle, highway, and communication infrastructures. It provides an electrified transportation system to support in-motion energy transfer through wireless charging of inductive coupling in the highway. A considerable body of previous research has sought to understand and improve the cooperative vehicle and existing infrastructure system. This research is one of the few studies that propose the frameworks that aim to simultaneously deal with both recent advances in vehicle automation and electrified highways to increase overall transportation system performance. The objective of this study is to develop an evaluation framework of the AET system. It focuses on three measures of effectiveness (MOEs): i) the system capacity, ii) energy savings, and iii) environmental emission reduction. They are examined based on simulated vehicle activity profiles. Results are provided to illustrate the performance of the system capabilities. Our results also contribute to an understanding of the key factors that can increase AET performance, and potentially impacts on future transportation mobility and sustainability.

Model for sustainable development based on system dynamics and energy–economy–environment coordination: A case study of Beijing, China

AbstractIn developing countries, the contradiction between environmental protection and economic development has become increasingly serious. The current research focuses on: how to coordinate the relationship between economic development and environmental protection. This study considers energy consumption and environmental emission reduction as the research object. An energy–economy–environment simulation system was developed using Vensim as a modeling platform to clarify the relationship between energy consumption, economic development, and environmental pollution. Based on the data gleaned from Beijing's energy system, the availability of the simulation model is verified through historical inspection and operational tests. Finally, based on the energy–economy–environment simulation system, different development scenarios have been set up to analyze the development trends of energy consumption, economic development, and environmental emission reduction under different scenarios. The results show that the development prospects under the coordinated development scenario are the most reasonable and effective, taking into account the coordinated development of all the three aspects, especially in terms of carbon emission reduction.

Energy trade market effect on production scheduling: an Industrial Product-Service System (IPSS) approach

ABSTRACT The demand response monitoring is one key energy-efficient production technique and can reduce costs contributing simultaneously to the reduction of environmental emissions and maintaining the stability of electrical grids following the policies examined from European and International organizations. Although, energy trade market effect has been investigated in the literature, the novelty of this research work lies in the design and development of an Industrial Product Service System (IPSS) business model, aiming to the collaboration between Energy Sales Companies (ESC) and manufacturing companies. ESC through an energy-demand management tool, supported by data acquisition devices connected to the Cloud, calculates and signals energy price forecasts to the manufacturers. Moreover, an adaptive production scheduling approach considering the power usage of manufacturers in response to time-varying energy prices is presented. Particularly, an intelligent algorithm is applied for the generation and examination of multi-criteria alternative assignment scenarios and an evaluation of each planning scenario against a set of user-defined criteria is made for the identification of the most efficient ones. The proposed approach is validated in an industrial case study using production and energy-consumption data from a mold making industry. The results indicate potential savings for the industrial consumer finding both actors to mutually benefit.

Development and Evaluation of Combined Adaptive Neuro-Fuzzy Inference System and Multi-Objective Genetic Algorithm in Energy, Economic and Environmental Life Cycle Assessments of Oilseed Production

Energy consumption, economics, and environmental impacts of canola production were assessed using a combined technique involving an adaptive neuro-fuzzy inference system (ANFIS) and a multi-objective genetic algorithm (MOGA). Data were collected from canola farming enterprises in the Mazandaran province of Iran and were used to test the application of the combined modeling algorithms. Life cycle assessment (LCA) for one ha functional unit of canola production from cradle to farm gate was conducted in order to evaluate the impacts of energy, materials used, and their environmental emissions. MOGA was applied to maximize the output energy and benefit-cost ratio, and to minimize environmental emissions. The combined ANFIS–MOGA technique resulted in a 6.2% increase in energy output, a 144% rise in the benefit-cost ratio, and a 19.8% reduction in environmental emissions from the current canola production system in the studied region. A comparison of ANFIS–MOGA with the data envelopment analysis approach was also conducted and the results established that the former is a better system than the latter because of its ability to generate optimum conditions that allow for the assessment of a combination of parameters such as energy, economic, and environmental impacts of agricultural production systems.

Environmental and process assessment of a vibratory nanofiltration system for the recovery of water from soluble coffee manufacturing waste

AbstractA case study has been conducted for the recovery of water from complex wastewater at a soluble coffee manufacturing factory. The study has evaluated separation methods for process intervention based on environmental and economic assessments. A novel vibratory field membrane separation was evaluated at the laboratory scale using actual factory wastewater, and was scaled‐up using appropriate design protocols. The recovery of water from an intermediate waste stream proved the most effective, both environmentally and economically. The proposed full‐scale vibratory membrane process recovers 378,541 L of water per day that meets specifications for reuse in the factory cooling tower. The proposed design reduced the daily well freshwater withdrawn by 21% and the amount of wastewater discharged from the factory by 28.5%. Annual operating costs were reduced by 22.5% and total life cycle emissions were reduced by 27.8%. These reductions are mainly the result of the reduced volume of wastewater discharged from the factory and the reduced energy requirement of the on‐site pretreatment processes. Economic assessment showed a net present value after 10 years is $181,100, while the payback time is under 3 years. Overall, the proposed vibratory membrane process used for water recovery presents favorable economics, and significant environmental emission reduction.

Haode Evaluating the Life-cycle Environmental Impacts of Polyester Sports T-shirts

Polyester fibre has a series of excellent properties and it has a wide range of uses. Recently China has become the largest producer and consumer of polyester in the world. The purpose of this paper is to analysis the environmental impacts of polyester shirts. Applying life cycle assessment to polyester textiles, through the analysis and research of resource utilization and environmental emissions during the life of polyester textiles, looking for opportunities to reduce environmental pollution, and providing reference for environmental management departments to develop environmental regulations and environmental emission reduction plans. The declared unit of this paper is a piece of T-shirt 100% made of polyeater fibre. Following the principles and guidelines of ISO series, this study collected both measured data of the client’s processes and secondary data. The data in this paper mainly comes from the literature, life cycle assessment (LCA) databases, and emission registration database of the government. The results of the study show that PET production, yarn manufacturing, and use-phases are the procedures that contribute the most environmental impacts. The total life-cycle energy use of a polyester sports shirt is 109 MJ, with 2882 litres of cumulative water use and contribution to global warming equivalent to 81.62 kg of CO2. The shirt’s use phase contribute the highest resources used, accounting for 65.5% of energy use, 86.71% of water use, and 84.31% of the global warming potential. The shirt production phase accounts for 30.85% of the energy use, 13.14% of total water use, and 12.99%of the global warming potential. Disposing phase contribute less among these process. The results of the study show that PET production, yarn manufacturing, and use-phases contributes to the majority of all environmental impacts. Polyester production and yarn manufacturing are steps that require the highest amount of energy. Utilizing recycled polyester fibres can reduce the net resource utilization and environmental impacts. A large amount of energy and water are used in the use-phase of a shirt. Improving the equipment efficiency can decrease the input energy and water consumption.

Environmental performance analysis on resource multiple-life-cycle recycling system: Evidence from waste pet bottles in China

Resource multiple-life-cycle (MLC) recycling process is a complex system with closed-loop and open-loop recycling interwoven with each other, which has recieved vast attention in recent years. Resource MLC recycling has a cascading effect, one recycling process will have an impact on the next cycle, and ultimately affect the overall environmental performance of the social system. Policies will affect the flow of resource MLC recycling. How to maximize the emission reduction effect of resource MLC recycling through policy design is still under exploration. Employing MLC recycling of China's waste PET bottles as an example, this paper constructs an environmental performance accounting method based on MLCA, and simulates the overall impact of various policies such as implementing deposit system, promoting recycled materials, strenghtening environmental regulation and enhancing regeneration technology on the recycling system of PET bottles. The combined policy scenario is constructed by selecting advantageous strategies that can effectively give full play to the strengths of each policy scenario. From 2020 to 2040, the environmental performance loss value of the combined policy scenario will be 52.52% of the baseline scenario without policy support, and the cumulative emission reductions of wastewater, waste gas and solid waste will be 0.98, 3.61 and 341.39 million tonnes respectively, which can help China to achieve emission reduction targets set out in each five-year plan. In order to realize the combined policy scenario, it is suggested that the life cycle assessment (LCA) should be replaced by multiple life cycle assessment (MLCA), the effect of resource saving and environmental emission reduction in the recycling process should be accounted as accurately as possible, a top-level management organization of natural resources across multiple departments should be established as soon as possible, with the goal of maximizing the comprehensive environmental and economic benefits of MLC processes.

Does the optimization of a company's environmental performance reduce its systematic risk? New evidence from European listed companies

AbstractThe objective of this study is to examine the relationship between a company's environmental performance and its systematic risk. Using a sample of 351 European listed companies over the 2007–2015 period, the technique of principal components analysis is applied to calculate a synthetic global index of corporate environmental performance using 71 evaluation criteria suggested by the ASSET4 database. Using this same approach, subindices of the global index are calculated using the following ASSET4 dimensions: reduction of emissions, product innovation, and reduction of resource usage. To test the relationship between these variables and risk, panel data regression techniques are applied. Results indicate that the synthetic global index of environmental performance negatively affects the systematic risk of company. The decomposition of this global index into three subindices shows that those subindices relating to the reduction of environmental emissions and of resource usage make it possible to significantly reduce systematic risk, whereas the subindex relating to product innovation has a rather moderate effect. Thus, environmental performance, like governance, can be considered as an insurance mechanism for the company, which reduces the probability of events occurring, which negatively affect its cash flow and risks faced by shareholders.

Sustainable food security decision-making: An agent-based modelling approach.

Ensuring a consistent and regular availability of food is crucial for food security. Food markets, supplied through both domestic production and international trade, are governed by several risks emerging from unpredictable supply chain disruptions, volatility of commodity prices, along with other unforeseen circumstances such as natural disasters. To mitigate the challenges threatening the stability of food systems, decision-making within the food sector should be enhanced and robust to accommodate any changes that might cause food shortages. Dynamic models, that can predict the behavior of food systems in order to avoid potential future knock-on effects and deficits, are incumbent to ensure the sustainable performance of food systems. This study proposes a dynamic decision-making scheme that simulates strategies of the perishable food market under different circumstances. An agent-based model (ABM) is developed and implemented using python MESA library for a case study in Qatar, illustrating the potential performance of tomato under three different scenarios to be considered, namely: (a) baseline scenario - aiming to reflect current production and market conditions; (b) water resource efficiency scenario - basing decisions on crop water requirement (CWR) depending on weather conditions; and (c) economic risk scenario - applying the concept of forward contracts to hedge against future uncertainties in crop prices. The findings of this study demonstrate that under the baseline conditions, a tomato crop can be supplied through a combination of domestic production and imports depending on the available inventories and prices imposed by exporters. The results obtained for the CWR scenario suggest the need for total reliance on imports in order to meet domestic demand, as there is potentially high-water loss, which amounts to an average of 4.9 Billion m3 per year, if tomato is grown locally. In contrast, the results from the forward contract scenario recommend a 57% dependency on local production in order to mitigate the effects of volatility in global food prices, which contributes to a 63% reduction in environmental emissions. Findings of this research provide insight into the factors that influence strategic decision making by the food sector to enhance its economic and environmental performances under diverse circumstances.

Integration of IoT technologies in construction supply chain networks; CPEC a case in point

China Pakistan Economic Corridor (CPEC) is a flagship project of China's Belt and Road (BRI) Initiative. Under the banner of CPEC, enormous developmental and infrastructure up-gradation projects are planned to be executed. These projects vary in their range and diversity from the development of a network of highways to up-gradation of electrical energy powerplants. Thus, the construction of these projects requires an operational and dedicated supply chain. This research study focuses on the identification of different internet of things (IoT) strategies that can be incorporated for increasing the efficiency of these supply chains. Fuzzy VIKOR, a multi-criteria decision-making technique (MCDM), has been employed for the prioritization of these strategies. These strategies are ranked on the basis of their cost-effectiveness, benefit to operational firms and partners, and social and environmental value. A total of twelve strategies are evaluated on the basis of the aforementioned criteria. The results of fuzzy VIKOR analysis prioritised the incorporation of IoT in equipment monitoring, reduction of environmental emissions, identification of security threats, augmented reality and inventory management as the most essential and effective strategies. Therefore, this study recommends the adoption of these strategies in CPEC projects on a priority basis.

Life-cycle assessment of solar integrated mining processes: A sustainable future

The negative impact of the mining industries on climate change, human health and ecosystems, arises from extensive use of fossil fuels. This could be mitigated by the integration of solar energy systems into mining processes, such as the use of solar industrial process heating systems in low-temperature mining processes. This paper estimates the resultant environmental impacts, which could result from the partial use of solar process heating in the aluminum, copper, ilmenite, rutile, lead, nickel, and uranium mining and extraction processes. The extensive analysis presented here contains multiple scenarios such as using flat plate solar collector and evacuated tube solar collector for process heat generation, including the base case scenario using fossil fuels. The novel analysis has quantified the overall reduction of environmental impacts on different categories of aquatic ecotoxicity, terrestrial ecotoxicity, carcinogens, non-carcinogens, and global warming. The analysis is done using SimaPro (version 8.5) utilising the IMPACT 2002 + method for 15 midpoints indicator-based categories and four endpoint indicator-based categories. The analysis results reveal valuable information regarding comparative impacts between the two types of solar collector considered and show that evacuated tube collectors would be more beneficial for environmental emission reduction.

Optimization of energy consumption and reduction of environmental emissions in cake production using data envelopment analysis and genetic algorithm

سابقه و هدف: توسعه پایدار ایجاب می­ کند که تأمین انرژی مورد نیاز بصورت پایدار، با هزینه ­ای قابل قبول و با کمترین اثر­های منفی اجتماعی و محیط زیستی صورت گیرد. بنابراین بهینه‌سازی مصرف انرژی و در نتیجه کاهش انتشار آلاینده­ های محیط زیستی از اهمیت ویژه‌ای برخوردار می‌باشد. هدف از این پژوهش بررسی میزان مصرف انرژی و انتشار آلاینده­ ها و در نهایت بهینه ­سازی مصرف انرژی با استفاده از روش ­های مرسوم بهینه ­سازی از جمله تحلیل پوششی داده­ ها و الگوریتم ژنتیک با هدف کاهش انتشارات محیط زیستی در صنعت تولید کیک در استان گیلان بود. مواد و روش ­ها: با استفاده از مدل ­های تحلیل پوششی داده ­ها، واحدهای کارا و ناکارا در مصرف انرژی شناسایی شدند. براساس الگوی مصرف انرژی واحدهای کارا، الگوی مصرف بهینه انرژی برای دیگر واحدها بیان شد و ارزیابی محیط زیستی براساس الگوی بهینه انجام شد. در­نهایت، با استفاده از روش الگوریتم ژنتیک و با در نظر گرفتن دو تابع هدف بمنظور افزایش عملکرد و کاهش شاخص گرمایش­ جهانی، الگوی بهینه مصرف انرژی در دیگر واحدهای تولید کیک بیان شد. نتایج و بحث: نتایج این پژوهش نشان داد که میزان 260532.25 مگاژول انرژی برای تولید روزانه 4157.14 کیلوگرم کیک مصرف شده است. بیشترین سهم انرژی مصرفی به گاز طبیعی با 128582.1مگاژول اختصاص داشت. همچنین سنجه گرمایش جهانی برای تولید هر تن کیک kg CO2 eq.13099.49 تعیین شد. بر اساس نتایج تحلیل پوششی داده­ ها، از مجموع 21 واحد تولیدی کیک، 17 واحد براساس مدل بازگشت به مقیاس متغیر کارا شناخته شدند. براساس نتایج مدل­ های تحلیل پوششی داده­ها، میزان کل انرژی موردنیاز در حالت مصرف بهینه نهاده ­ها، درصد صرفه‌جویی انرژی و کاهش سنجه گرمایش جهانی بترتیب برابر 254929.28 مگاژول در روز، 2.15 درصد و kg CO2 eq. 550.18 به ازای تولید یک تن کیک تعیین شدند. همچنین الگوی مصرف انرژی بیان شده توسط الگوریتم ژنتیک چند هدفه منجر به کاهش 36.30 درصدی در مصرف انرژی شد که بیشترین درصد صرفه ­جویی در انرژی مربوط به نیروی کارگری بود. بر اساس نتایج بهینه ­سازی چندهدفه، میزان گرمایش جهانی به ازای تولید یک تن کیک برابر kg CO2 eq. 10038.44 تعیین شد. نتیجه ­گیری: بهینه‌سازی چند­هدفه با الگوریتم ژنتیک در مقایسه با بهینه­ سازی با تحلیل پوششی داده­ ها، منجر به کاهش بیشتر انرژی مصرفی، گرمایش ­جهانی، هزینه ­های تولید و بارهای محیط زیستی و همچنین افزایش بیشتر درآمد شد. بنابراین استفاده از روش الگوریتم ژنتیک، راه را برای رسیدن به توسعه پایدار در صنعت تولید کیک و باقی­ماندن در عرصه رقابت با دیگر صنایع غذایی هموار خواهد کرد.