With the increase in pollution and people’s awareness of the environment, reducing greenhouse gas (GHG) emissions from products has attracted more and more attention. Companies and researchers are seeking appropriate methods to reduce the GHG emissions of products. Currently, product family design is widely used for meeting the diverse needs of customers. In order to reduce the GHG emission of products, some methods for low-carbon product family design have been presented in recent years. However, in the existing research, the related GHG emission data of a product family are given as crisp values, which cannot assess GHG emissions accurately. In addition, the procurement planning of components has not been fully concerned, and the supplier selection has only been considered. To this end, in this study, a concurrence optimization model was developed for the low-carbon product family design and the procurement plan of components under uncertainty. In the model, the relevant GHG emissions were considered as the uncertain number rather than the crisp value, and the uncertain GHG emissions model of the product family was established. Meanwhile, the order allocation of the supplier was considered as the decision variable in the model. To solve the uncertain optimization problem, a genetic algorithm was developed. Finally, a case study was performed to illustrate the effectiveness of the proposed approach. The results showed that the proposed model can help decision-makers to simultaneously determine the configuration of product variants, the procurement strategy of components, and the price strategies of product variants based on the objective of maximizing profit and minimizing GHG emission under uncertainty. Moreover, the concurrent optimization of low-carbon product family design and order allocation can bring the company greater profit and lower GHG emissions than just considering supplier selection in low-carbon product family design.

Development of a low-carbon product design system based on embedded GHG emissions

Innovative product design method for low-carbon footprint based on multi-layer carbon footprint information

Greenhouse gas emission has become a recent global concern for green manufacturing. As product low-carbon design is an essential approach to achieve low-carbon manufacturing, which has a profound effect on the product carbon footprint, many researches have been focused on it in recent years with a result of valuable contributions. This paper is devoted to presenting a dynamic programmingbased approach to product low-carbon design. After product low-carbon design is characterized by a multi-stage decision process with interaction effects on each other in the product life cycle, a dynamic programming method is used to optimize the total carbon footprint of each stage while considering interaction effects of solutions at each stage in product life cycle. The low-carbon design of a cold heading machine is used to demonstrate the proposed methodology.

This paper presents a conceptual product design method that considers the carbon emission factors of mechanical and electrical products. This method aims to consider low carbon issues prior to product design. Based on a comprehensive analysis of conventional product design procedures, the study extracts design information related to greenhouse gas (GHG) emissions, itemizes and quantifies the information, and ultimately uncovers the relationship among carbon emissions, function, and economic factors of newly designed products. This study explains the proposed concepts of carbon efficiency (CE) and carbon factor and combines them with the product design process to present a low-carbon design process of mechanical and electrical products. Furthermore, this paper presents a low-carbon design method of mechanical and electrical products, drawing on the product family design idea, with GHG efficiency and cost constraint. In particular, GHG efficiency pays attention to product function realization efficiency and economic factors. The potential value of the method is demonstrated in a case study of a tomato picking robot.

In recent years, there has been a call for a shift to transportation with lower greenhouse gas (GHG) emissions in order to combat global warming. One of the ecofriendly transportation methods is an electric moped scooter (e-moped)-sharing service that does not emit GHG when it runs. It is necessary to plan the location of charging stations and the material procurement through the manufacturing of e-mopeds in order to reduce the cost and GHG emissions and to improve the accessibility of the service. In this study, a two-stage design on the e-moped-sharing services is proposed to allocate charging stations and select material suppliers for e-mopeds using integer programming. The analysis method to determine the suitable charging station locations and sizes and supplier selection are also presented. Numerical experiments are conducted to illustrate the proposed design and analysis method by assuming Kumpan’s 1954 i model installation in Bochum city, Germany. In the numerical experiments, set covering and maximal covering location problems with small coverage radius of charging stations would be better by evaluating accessibility, GHG emissions, and cost comprehensively. Moreover, 11 prioritized demand points were picked out by introducing new indexes such as geographical and demand importance.

Major US electric utility climate pledges have the potential to collectively reduce power sector emissions by one-third

Livestock production is under growing public and scientific scrutiny for its greenhouse gas (GHG) emissions. This article contains a preliminary assessment of the inclusion of upstream life-cycle GHG emissions in concentrated feeds design, using the most common nonlinear programming optimization algorithms to determine feed composition. First, GHG emissions are included as costs in a single criteria optimization problem. The unit price of GHG emissions was obtained using a genetic algorithm. Second, GHG emissions are included as a target function to minimize in a multi criteria optimization problem using goal attainment programming. Results obtained after both optimization methods were applied to two case studies, namely fattening pigs and rabbit feeds. Changing ingredients in concentrated feed blends has a marginal effect on GHG emissions due to mandatory nutritional constraints. If the optimization is unconstrained, the maximum possible decrease in GHG emissions is 27.5% for the pigs feed, accompanied by increasing costs and a decrease in feed nutritional quality. To maintain nutritional integrity, the maximum possible reduction in GHG emissions is 7.5%. Considering cost as an optimization variable in the problem, the maximum decreases are even lower. It is possible to decrease emissions by 71% for the rabbits feed, but the cost of the reduction is higher than the opportunity cost for farmers to reduce GHG emissions using other strategies. These results are qualitatively robust but critically depend on feed ingredients GHG emissions and cost data.

Energy-related activities are a major contributor of greenhouse gas (GHG) emissions. A growing body of knowledge clearly depicts the links between human activities and climate change. Over the last century the burning of fossil fuels such as coal and oil and other human activities has released carbon dioxide (CO2) emissions and other heat-trapping GHG emissions into the atmosphere and thus increased the concentration of atmospheric CO2 emissions. The main human activities that emit CO2 emissions are (1) the combustion of fossil fuels to generate electricity, accounting for about 37% of total U.S. CO2 emissions and 31% of total U.S. GHG emissions in 2013, (2) the combustion of fossil fuels such as gasoline and diesel to transport people and goods, accounting for about 31% of total U.S. CO2 emissions and 26% of total U.S. GHG emissions in 2013, and (3) industrial processes such as the production and consumption of minerals and chemicals, accounting for about 15% of total U.S. CO2 emissions and 12% of total ...

Taking Stock of Strategies on Climate Change and the Way Forward: A Strategic Climate Change Framework for Australia

BackgroundPolicies to mitigate climate change by reducing greenhouse gas (GHG) emissions can yield public health benefits by also reducing emissions of hazardous co-pollutants, such as air toxics and particulate matter. Socioeconomically disadvantaged communities are typically disproportionately exposed to air pollutants, and therefore climate policy could also potentially reduce these environmental inequities. We sought to explore potential social disparities in GHG and co-pollutant emissions under an existing carbon trading program—the dominant approach to GHG regulation in the US and globally.Methods and findingsWe examined the relationship between multiple measures of neighborhood disadvantage and the location of GHG and co-pollutant emissions from facilities regulated under California’s cap-and-trade program—the world’s fourth largest operational carbon trading program. We examined temporal patterns in annual average emissions of GHGs, particulate matter (PM2.5), nitrogen oxides, sulfur oxides, volatile organic compounds, and air toxics before (January 1, 2011–December 31, 2012) and after (January 1, 2013–December 31, 2015) the initiation of carbon trading. We found that facilities regulated under California’s cap-and-trade program are disproportionately located in economically disadvantaged neighborhoods with higher proportions of residents of color, and that the quantities of co-pollutant emissions from these facilities were correlated with GHG emissions through time. Moreover, the majority (52%) of regulated facilities reported higher annual average local (in-state) GHG emissions since the initiation of trading. Neighborhoods that experienced increases in annual average GHG and co-pollutant emissions from regulated facilities nearby after trading began had higher proportions of people of color and poor, less educated, and linguistically isolated residents, compared to neighborhoods that experienced decreases in GHGs. These study results reflect preliminary emissions and social equity patterns of the first 3 years of California’s cap-and-trade program for which data are available. Due to data limitations, this analysis did not assess the emissions and equity implications of GHG reductions from transportation-related emission sources. Future emission patterns may shift, due to changes in industrial production decisions and policy initiatives that further incentivize local GHG and co-pollutant reductions in disadvantaged communities.ConclusionsTo our knowledge, this is the first study to examine social disparities in GHG and co-pollutant emissions under an existing carbon trading program. Our results indicate that, thus far, California’s cap-and-trade program has not yielded improvements in environmental equity with respect to health-damaging co-pollutant emissions. This could change, however, as the cap on GHG emissions is gradually lowered in the future. The incorporation of additional policy and regulatory elements that incentivize more local emission reductions in disadvantaged communities could enhance the local air quality and environmental equity benefits of California’s climate change mitigation efforts.

Greenhouse gas emission has become a recent global concern for green manufacturing. As the production is one of the main greenhouse gas emission sources, more and more researches have recently begun to pay attention to product low-carbon design. However, the traditional product modeling is easy to produce an obstacle for low-carbon design and lack of an effective integrated modeling approach to support knowledge reuse in product low-carbon design. This paper is devoted to presenting product model integrated with carbon footprint. After the carbon footprint meta-model is proposed, the product model integrated with carbon footprint for low-carbon design is proposed. And the corresponding modeling principles for product model are also discussed in detail. The product model of a magneto-rheological fluid absorber integrated with carbon footprint is used to demonstrate the proposed methodology.

Product low carbon design plays a significant part in reducing carbon emissions during the product life cycle and achieving sustainable development. Aiming at the uncertainty of the carbon footprint caused by the unknown decision of product material solution, this paper proposes a low carbon design approach that uses the multigraph and colour marking to solve the problem of one edge assigned with multiple weights and the constraint problem of vertex combination in the decision space of product low carbon design. After a weighted edge-coloured graph (WECG) is proposed for low carbon design, the multi-stage product low carbon design model for product life cycle is discussed in detail. An algorithm of searching the alternative set of optimal design is also put forward in detail, including to create the directed WECG, to define the complete path, to find the ideal solution set using breadth-first search, to judge the ideal solution set by colouring the graph to obtain the feasible paths, and to rank paths in the ideal solution set in ascending order. The low carbon design of a robot is given as an example to demonstrate the methodology.

Well-to-wheel greenhouse gas emissions of electric versus combustion vehicles from 2018 to 2030 in the US

Low-carbon and Economic Supplier Selection Using Life Cycle Inventory Database by Asian International Input-Output Tables