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Product service systems (PSS) are an example of a novel business model billed as having the potential to significantly reduce the environmental burdens of production and consumption processes. However, despite widespread interest in PSS, consensus regarding their actual environmental impacts, particularly with regard to salient issues such as global warming, is lacking. Hence this paper explores existing research to investigate the state of the art regarding the climatic impacts of PSS, alongside the set of factors that influence climatic impacts. The paper comprises a systematic review of peer-reviewed academic literature, quantifying the extent to which different types of PSS have the capacity to reduce greenhouse gas emissions across multiple product categories. Our study shows that significant reductions in climatic emissions are possible, but PSS are in many cases associated with more modest reductions and, in some cases, increased emissions. Further, we observe no clear differences in climatic impacts according to the type of PSS model that is deployed. Rather, differences in climatic impact are influenced by factors such as production and design alongside use-phase impacts and contextual factors such as transportation and the energy mix. The study argues that further research is needed to establish a more robust baseline upon which to draw conclusions regarding the sources of climatic impacts, and outlines fruitful ways for companies to tackle the complexities of climatic emissions that are beyond their control. • In some cases, product service systems can reduce climatic impacts by up to 80%. • In other cases, climatic impacts increase compared to traditional product sales. • Climatic impacts are influenced by use intensity, transportation and production. • Other contextual factors such as the energy mix play a role. • More quantitative assessments are needed with less reliance on estimations. [ABSTRACT FROM AUTHOR]

Abstract Improving rural household energy efficiency is an important policy issue for climate change mitigation in China. A better understanding of the factors affecting farmers' energy-efficient appliance use behavior can help policymakers design more effective policies. This paper explores the effect of farmers' risk preferences and loss aversion on their energy-efficient appliance use behavior in rural China. Using unique data from a survey and a paired lottery experiment completed by 235 rural household heads in the Dazu District of China, this study finds that the farmers' risk preferences and loss aversion have significant effects on their energy-efficient appliance use behavior. The more risk-averse farmers are less likely to buy or use energy-efficient appliances. The farmers who are more loss averse are more willing to purchase and more likely to use durable energy-efficient appliances. In addition, the farmers' demographic factors (age, gender, education, and family location), their perceptions of climate change adaptation and their trust attitudes have significant effects on their energy-efficient appliance use behavior. This paper contributes to the emerging literature that relates risk preferences and loss aversion in experiments to farmers' energy-efficient appliance use behavior. Highlights • The analysis is based on a unique data set combining a survey and economic experiments. • The average response of our sample is risk averse and loss averse. • The more risk-averse farmers are less likely to use energy-efficient appliances. • The more loss-averse farmers are more willing to use durable energy-efficient appliances. • Farmers' socioeconomic status and trust attitudes affect their use behavior. [ABSTRACT FROM AUTHOR]

Abstract: A number of empirical methods have been developed to study China's sectoral carbon emissions (CSCE). Measuring these emissions is important for climate change mitigation. While several articles have reviewed specific methods, few attempts conduct a systematic analysis of all the major research methods. In total 807 papers were published on CSCE research between 1997 and 2017. The primary source of literature for this analysis was taken from the Web of Science database. Based on a bibliometric analysis using knowledge mapping with the software CiteSpace, the review identified five common families of methods: 1) environmentally-extended input-output analysis (EE-IOA), 2) index decomposition analysis (IDA), 3) econometrics, 4) carbon emission control efficiency evaluation and 5) simulation. The research revealed the main trends in each family of methods and has visualized this research into ten research clusters. In addition, the paper provides a direct comparison of all methods. The research results can help scholars quickly identify and compare different methods for addressing specific research questions. [ABSTRACT FROM AUTHOR]

Biscuits are a major product category in the global confectionary sector. Despite this, their environmental impacts are poorly characterised. Therefore, this paper sets out to evaluate the life cycle environmental sustainability of the following widely-consumed types of biscuit, both at the product and sectoral levels: crackers, low fat/sugar, semi-sweet, chocolate-coated and sandwich biscuits with chocolate or vanilla cream. The results obtained through life cycle assessment demonstrate that, in addition to being healthier, low fat/sugar biscuits have the lowest impacts across most of the 18 categories considered. Chocolate-coated biscuits are environmentally the least sustainable. The most significant life cycle stage for all types is the raw materials production, causing 41%–61% of the total impacts, with flour, sugar and palm oil being the key hotspots. Replacing palm with rapeseed oil would improve five impacts but worsen another five, including a 34% increase in agricultural land occupation and marine eutrophication. Therefore, the cultivation and production of palm oil, rather than its replacement, should be targeted for improvements. The second most crucial stage is manufacturing, contributing up to 54% of the impacts, followed by transport with up to 35%. Reducing energy consumption by 25% in manufacturing would reduce primary energy demand by 8%–12%, fossil fuel depletion by 9%–12% and global warming potential by 6%–9%. The latter would increase by 55% if land use change related to cocoa was involved, despite a very small amount of cocoa in the biscuits (∼1%). The analysis at a sectoral level in the UK, the leading consumer of biscuits in Europe, reveals that biscuits contribute 7.4% of primary energy demand and 0.5% of greenhouse gas emissions of the whole UK food sector. These results can help guide manufacturers in mitigating the hotspots in the supply chain and consumers in selecting environmentally more sustainable biscuits. • The environmental sustainability of six types of biscuit is evaluated for the first time. • Low fat/sugar biscuits have the lowest impacts and chocolate-coated the highest. • Global warming potential of chocolate biscuits is 55% higher with land use change. • Flour, sugar and palm oil are the key hotpots, followed by biscuits manufacturing. • Biscuits contribute 7.4% of energy demand and 0.5% of GWP of the UK food sector. [ABSTRACT FROM AUTHOR]

Abstract Climate change has caused worldwide concern due to its adverse effects on the global ecosystem, economy and society. In this study, a new integrated framework was proposed for assessing the national vulnerability to climate change by considering both the sensitivity and adaptive capacity. Then, a first assessment of the spatial-temporal change in national vulnerability from 1996 to 2008 was provided from the global perspective. Finally, 171 countries were grouped, and hotspots of climate change were identified. Based on the results, the earth is more vulnerable than invulnerable, and the average rate of increase in the vulnerability index from 1996 to 2008 was 0.30%. African countries were identified as hotspots of vulnerability and instability in the changing climate, and 8 groups were clustered based on the key influencing index of 171 countries. The results of this new framework are consistent with the previous Fragile State Index and World Risk Index but provide more details and quantitative vulnerability analysis. The methods and results presented in this paper could be used as references for climate change adaption and policy at the global scale. Graphical abstract Image 106550 Highlights • A new method was proposed for assessing the vulnerability to climate change. • The spatial-temporal change in national vulnerability was provided. • Urrent earth become more vulnerable to climate change than ever before. • Key influencing factor were identified for effective adaption to climate change. • Countries were grouped, and hotspots of climate change were identified. [ABSTRACT FROM AUTHOR]

Abstract Climate change, characterized with global warming, created severe potential threat to natural ecosystems and sustainable development of human society. Effective adaptation measures to mitigate the negative effects of climate change would affect both the ecological environment and the social economy. In this paper, we built a positive mathematical programming model to assess adaptation measures, which were divided into four aspects, to tackle climate change based on the survey data obtained from seven counties and 32 villages in the region of Hulun Buir. The results showed that, livestock breeding improvement had a positive impact on grass yield, livestock number and especially herder's household income (the most in Ewenki (11.7%) and the least in Xinzuo (2.3%)); artificial grassland project could lead to an average growth rate of 8.4% in household income among the four counties; current policies and going out for work could also increase household income, however, not a sustainable choice. To realize sustainable development in the context of global climate change, the vitality of sustainable development of animal husbandry under the premise of protecting ecological environment should be enhanced. For example, improving the safety and quality of livestock products through influencing livestock breeding, processing technology and high quality forage, and ensuring adequate forage supplement through artificial grassland project. Highlights • The pastoral regions in Hunlun Buir have a poor capability for resisting natural disasters. • We built a positive mathematical programming model to assess adaptation measures in a pastoral region. • Livestock breeding improvement and artificial grassland project had a positive impact on income. • Sustainable development of animal husbandry under the premise of protecting ecological environment should be enhanced. [ABSTRACT FROM AUTHOR]

Global warming may cause the average atmospheric temperature to exceed 2 °C by the end of this century, instigating climate change and adversely affecting human lives. Emissions of carbon dioxide (CO 2) should be reduced to overcome the problems of global warming and climate change. The applications of negative emission technologies (NETs) are proposed to reduce terrestrial CO 2 and are identified as one of the prominent options for the energy system transition. Most negative emission technologies require energy to function, which has to be produced from additional energy sources. Understanding the interdependence of the energy sources and NETs for planning the carbon-constrained energy sector is essential. A novel graphical approach, founded on the concepts of Pinch Analysis, to decarbonize the energy sector is purported in this paper. The proposed method determines the minimum integration of negative emission technologies required to attain the allowed CO 2 emission limit while satisfying the energy demand. Four different examples are used to show the applicability of the suggested approach with different NETs: carbon capture and sequestration (CCS), bioenergy with carbon capture and storage (BECCS), and direct air capture (DAC). In these examples, renewable-based energy sources, fossil-based energy sources, as well as energy-producing negative emission technologies are used as compensatory energy sources. The proposed graphical approach has the potential to be used as a planning tool to decarbonize the energy sector involving interdependent energy sources and NETs. [Display omitted] • Negative emission technologies help decarbonize the energy sector. • Identified dependence between the energy sector and negative emission technologies. • A graphical method is proposed to integrate negative emissions technologies. • Determined minimum compensatory energy for adopting negative emission technologies. • The proposed graphical approach provides insight into the overall systems planning. [ABSTRACT FROM AUTHOR]

Global warming, climate change, and resource depletion have forced us to reconsider energy usage and efficiencies over the last few decades. Residential and commercial buildings are both large energy consumers, so improving energy and material usage efficiency in this sector is a common research topic. According to a recent study, the Building Sector (BS) accounts for 40% of greenhouse gas emissions. The primary objective of this paper is to examine and assess the potential of Renewable Energy Systems (RES) and their combinations for enhancing energy efficiency in the BS. Specifically, the focus will be on converting low energy-efficient buildings into highly efficient ones. The potential of the RES and their combinations for the BS is evaluated based on payback durations, energy generation, and reduction of CO 2 emissions. The optimization flow charts for the RES, feasibility studies, commercialization road maps of energy storage systems and the necessity of control mechanisms for enhancing RES efficiency were discussed. Additionally, the technology drawbacks are discussed, along with various innovative techniques recommended to direct future study in this area. Finally, this article assists the audience clear idea in the selection of the right combination of potential RES based on different conditions to achieve deep decarbonization in BSs. • Buildings sector (BS) responsible for 40% of GHG emissions. • Study assesses potential of Renewable Energy System (RES) in BS. • RES evaluated on payback, energy generation, and CO 2 reduction. • Optimization, feasibility and commercialization studies were discussed. • Article recommends right RES combinations for deep decarbonization in BS. [ABSTRACT FROM AUTHOR]

With the confluence of rapidly continuing economic development and increasing greenhouse gas emissions, the frequency and severity of natural disasters caused by global warming have increased in recent years. The international Financial Stability Board established the Task Force on Climate-Related Financial Disclosures (TCFD) to provide recommendations for collecting and disclosing climate-related information that is helpful for decision-making such that investors, decision-makers and supervision units can understand and more accurately assess climate-related risks as well as opportunities. However, the TCFD recommendations only provide a general framework of risks and opportunities from which companies cannot derive specific disaster-related data for regional climate disclosures. For stakeholders in different continents and regions, there is a need to develop a disaster data-driven model that can precisely disclose regional risks. Our study develops a regional climate framework for TCFD based on natural language processing that can process and analyze data from historical local news. The paper aims to offer suitable climate recommendations for different regions and suggest possible preventive measures and responses to emergencies caused by various disasters. The contributions of this research contain: (1) using unsupervised learning to present climate change in a quantitative way; (2) helping enterprises adjust sustainable strategies according to conditions in different regions; (3) extending involvement from a small area to a large area, quantifying climate data so that investors can make decisions based on factual results; (4) combining artificial intelligence with climate change in the field of TCFD. [Display omitted] • We develop a data-driven regional climate framework for TCFD based on deep learning. • This framework can help enterprises minimize risks along with costs and can be applied to other industries. • This research supplements quantitative information in TCFD and provides a scientific climate risk management method. • The first study that applies artificial intelligence to climate change in the field of TCFD. [ABSTRACT FROM AUTHOR]

GHG emissions are important footprints because of increasing concern over climate change. Remediation methods produces GHGs in varying quantities based on which activities are involved through its entire life cycle and which contaminants are present, often because some contaminants are more difficult to remove from soil than others. Accounting for emissions from all phases of the project requires a life cycle assessment (LCA) approach. LCA can help in choosing the best available technology to reduce the environmental burden of the remediation technology or to improve the sustainability of the technology by implementing systematic approaches to ensure that future developments are optimized for environmental performance throughout the life cycle. The primary objective of this paper is to review existing LCA studies that report GHG emissions (CO 2-eq ) or Global warming potential (GWP) from six ex-situ soil remediation technologies (ESRTs), including excavation and disposal, ex-situ thermal desorption, ex-situ soil vapor extraction, ex-situ bioremediation, excavation and incineration, and soil washing, and present the variability in GHG/GWP results and how this data can help in selecting an ex-situ soil remediation technology with a lower global warming potential. A second objective of this study is to compare the GWP levels of ex-situ remediation to the GWP levels of typical in situ remediation methods. Our results showed a large variation in GHG emissions of treated soil from six ESRTs varying from 3.1 × 10 −7  t to 8.2 t CO 2 -eq/m 3 . Incineration had the highest mean GHG emissions (0.7 t CO 2 -eq/m 3 ) and thermal desorption the lowest (0.07 t CO 2 -eq/m 3 ). It was also found that there was a large variation range of GHG emissions from the ex-situ excavation and disposal method soil treatment technologies, varying from 3.1 × 10 −7  t to 8.2 t CO 2 -eq/m 3 of treated soil. This knowledge provides opportunities to increase sustainability of soil remediation throughout the investigation, design, construction, operation, and monitoring phases of site remediation regardless of the selected cleanup remedy. This shows data on LCA GHGs are useful to assess the impact of different scenarios and management practices on ESRTs. [ABSTRACT FROM AUTHOR]

The so-called Paris Agreement, signed by 195 countries, has led to the setting of some ambitious target reductions in greenhouse gas emissions by most developed countries. The over-arching goal of those reduction targets is to limit global warming to less than 2 o C compared to pre-industrial levels. Yet, despite these commitments from the highest governance levels, there is still no deterministic model on how these national target reductions will be cascaded down to the commercial sector, which accounts for the lion's share of these emissions. In this paper, we present a novel framework which enables the setting, measurement and tracking of the emissions performance of every commercial entity based on a single metric. We further present empirical evidence from the automotive industry to illustrate how the model helps to readily identify the low versus high performers. The model also reveals one car company that stands out as the industry benchmark by already exceeding the performance required by the 2025 emissions target set by the US. The model proves successful in achieving the objectives of being: science-based, goal-driven, equitable, comparable and actionable, and as such can be readily used to accurately compare competitors from the same industry and provide the basis of a more equitable, performance-based carbon tax market, as well as the ability to assess accurately how well a specific country is meeting its reduction quota and how the various industry sectors are impacting that goal. [ABSTRACT FROM AUTHOR]

The recent climate change, global warming, environmental disasters and the economic crisis are only the first signs of the failure of an economic system that, for too long, shows an uncontrolled utilization of the planet wealth. The Italian electricity market, which is strongly dependent on hydrocarbons, only in recent years has seen a first attempt to change towards renewable resources for electricity production aimed at self-consumption and for feeding into the grid. This paper presents an economic analysis whose purpose is to evaluate the profitability of investments in renewable technologies for the production of electricity. Each renewable source has its own profitability dependent on a number of factors and subject to market fluctuations, cost and frequent changes in the incentive policies. Applying Portfolio Theory is possible to select the right mix of renewable energy sources and simulate its evolution. Moreover the presented analysis can be useful for energy planners to select future green scenarios finalized to the reduction of emissions and energy imports through the increasing use of renewable energy. [ABSTRACT FROM AUTHOR]

Geoengineering, which is the intentional large-scale manipulation of the environment, has been suggested as an effective means of mitigating global warming from anthropogenic greenhouse gas emissions. In this paper, we will review and assess technical and theoretical aspects of land-based, atmosphere-based, ocean-based and space-based geoengineering schemes as well as their potential impacts on global climate and ecosystem. Most of the proposed geoengineering schemes carried out on land or in the ocean are to use physical, chemical or biological approaches to remove atmospheric CO 2 . These schemes are able to only sequester an amount of atmospheric CO 2 that is small compared with cumulative anthropogenic emissions. Most of geoengineering schemes carried out in the atmosphere or space are based on increasing planetary albedo. These schemes have relatively low costs and short lead times for technical implementation, and can act rapidly to reduce temperature anomalies caused by greenhouse gas emissions. The costs and benefits of geoengineering are likely to vary spatially over the planet with some countries and regions gaining considerably while others may be faced with a worse set of circumstances than would be the case without geoengineering. Since current research on geoengineering is limited and various international treaties may limit some geoengineering experiments in the real world, the Geoengineering Model Intercomparison Project (GeoMIP) provides a framework of coordinated experiments for all earth system modeling groups to test geoengineering schemes. However, these experiments used on a global scale have difficulty with accurate resolution of regional and local impacts, so future research on geoengineering is expect to be done by combining earth system models with regional climate models. [ABSTRACT FROM AUTHOR]

Low carbon manufacturing is of growing importance due to the severity of energy shortage and increasing energy costs, as well as a series of environmental problems, i.e., climate change and global warming. This paper investigates the potential of process parameter optimization to minimize carbon emissions during machine processing. A process model of CNC machining is first presented to scope the system boundaries for energy footprint and process efficiency. A multi-objective optimization model is then proposed to explore the impact of cutting speed and feed rate on carbon emissions and processing time. The benefit of the approach is illustrated through a case study of a CNC cylindrical turning process. [ABSTRACT FROM AUTHOR]

Various environmental regulations put ever stringent requirements on the automotive industry as a part of solution to the problem of global warming and climate change. In Europe, the upcoming Euro 6 standard that will come into force on September 1, 2014 demands reduction in NO x emissions from cars and vehicles intended for transport by more than 50% compared to the current standard. The urea-water-solution (UWS) based selective catalytic reduction (SCR) is currently the most promising method for fulfilling these requirements. In principle, the UWS spray is injected into a hot exhaust gas stream preceding the SCR catalyst and ammonia is generated through series of chemical reactions. Then, the generated ammonia acts in various deNO x reactions as a reductant. This paper investigates application of numerical modeling in the area of environmentally friendly technology of mobile SCR as a green engineering measure to reduce pollution in the road transport sector. Within this work the mathematical model of surface tension has been developed and compared to analytical expression for isothermal droplet spreading. Furthermore, this model was incorporated into the wall film module of the commercial computational fluid dynamics code FIRE for description of urea-water-solution injection into hot exhaust gases of diesel engine and compared to experimental data. Results of the conducted numerical study support the feasibility of commercial application of the presented model. [ABSTRACT FROM AUTHOR]

Due to the significant effects of CO 2 emissions on climate change and global warming, as well as its serious hazards to human health, the prediction of CO 2 emission is a vital issue. The main aim of this paper is to evaluate the power of the Inclusive Multiple Model (IMM) as a novel approach to predict CO 2 emission in the agriculture sector of Iran. For the same, we implemented the environmental Kuznets curve (EKC) specification and data from 2003 to 2017 for 28 provinces of Iran. In the first level, various specifications were implemented for each of the Multiple Regression (MLR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) models. In the second level, an IMM model was implemented for treating the outputs of the best specification out of the MLR, GPR, and ANN models as inputs to an ANN model. The performance of the models was compared with the Taylor diagram and innovation and unique graphs. Findings indicated that the IMM model with C C = 0.81 , RMSE = 0.69, the highest residuals between −5 and 5 (37.84%), and the lowest distance from observation points (1.857) estimated CO 2 emission values more precisely. These improvements indicate that there are possible directions for future research activities. Due to the most accuracy of the IMM, it is recommended to use this method to predict CO 2 emission to adopt appropriate policies for reducing air pollution. Image 1 • We developed Inclusive Multiple Model (IMM) as a novel approach to predict CO 2 emission in the agriculture sector of Iran. • We utilized Environmental Kuznets Curve (EKC) hypothesis literature to predict CO 2 emission. • We used the Taylor diagram with distance and new graphs to determine the most accurate model. • A measure of income inequality could improve results in investigating EKC. • The IMM was the most accurate model according to all of performance metrics and diagnostic graphs. [ABSTRACT FROM AUTHOR]