A roadmap for China to peak carbon dioxide emissions and achieve a 20% share of non-fossil fuels in primary energy by 2030

As part of its Paris Agreement commitment, China pledged to peak carbon dioxide (CO2) emissions around 2030, striving to peak earlier, and to increase the non-fossil share of primary energy to 20% by 2030. Yet by the end of 2017, China emitted 28% of the world's energy-related CO2 emissions, 76% of which were from coal use. How China can reinvent its energy economy cost-effectively while still achieving its commitments was the focus of a three-year joint research project completed in September 2016. Overall, this analysis found that if China follows a pathway in which it aggressively adopts all cost-effective energy efficiency and CO2 emission reduction technologies while also aggressively moving away from fossil fuels to renewable and other non-fossil resources, it is possible to not only meet its Paris Agreement Nationally Determined Contribution (NDC) commitments, but also to reduce its 2050 CO2 emissions to a level that is 42% below the country's 2010 CO2 emissions. While numerous barriers exist that will need to be addressed through effective policies and programs in order to realize these potential energy use and emissions reductions, there are also significant local environmental (e.g., air quality), national and global environmental (e.g., mitigation of climate change), human health, and other unquantified benefits that will be realized if this pathway is pursued in China

Assessing potential reduction in greenhouse gas : an integrated approach

Abstract: Greenhouse gases remain as threat to the environment. Various models employed in greenhouse gases are either to determine the causative factors responsible for emission, forecast emission or to optimize. Integrating these models would reduce the limitations of individual models to better assess possible greenhouse mitigation. This paper addresses the management technique for analyzing, assessing and mitigating industry’s carbon dioxide (CO2) emission. The current work offers a different technique based on an integrated model utilizing the functions of Index Decomposition Analysis (IDA), Artificial Neural Network (ANN) and Data Envelopment Analysis (DEA) composed of activity, structure, intensity and energy-mix as inputs responsible for CO2 emission. By considering how the three different models are integrated into one system, it will be demonstrated how much percentage of an industry’s CO2 can be reduced. The Canadian industrial sector was analyzed using the integrated model and it was discovered that 3.13% of emitted CO2 from year 1991 to year 2035 could be mitigated

Optimized non-linear multivariable grey model for carbon dioxide emissions in Malaysia

This paper analyses the relationship between carbon dioxide emissions with the energy consumption from the year 2005 to 2014 in Malaysia by introducing an optimized non-linear multivariable grey, NGM(1,N) model by establishing a power exponent term for its subsequent relevant factors. The aim of this research is to improve the existing NGM(1,N) model by solving the effect of non-linear properties which is able to correlate among the consequent factors based on the selection of power exponent optimization. This paper will also introduce the transformed NGM(1,N) known as TNGM(1,N) model that produces a more accurate result compared to NGM(1,N) model that prompted simulated output. The power exponent term value was determined using the generalized reduced gradient (GRG) method in Microsoft Excel Solver. It is proven that the TNGM(1,N) model performs the best and hence it serves as vital information for the government's environmental-related agencies and policymakers to focus on the effort to promote green efficient technology to society at large by reducing the releases of carbon dioxide emissions to the environment

How would big data support societal development and environmental sustainability? Insights and practices

The theme of this Special Volume (SV) focuses on improving natural resource management and human health to ensure sustainable societal development. Natural resources have been exploited unduly regardless of the consequences, which has resulted in inappropriate management natural resources and has caused severe environmental degradation. Contributions in this SV addressed improved environmental management, utilization, and allocation of natural resources; evaluation of sustainable natural resource management; pollution prevention and treatment; and evaluation and suggestions for improved natural resource-related policies. The authors presented an inspiring panorama of the initiatives that have been developed throughout the world for sustainable natural resource management and improve societal development. Theoretically, new approaches to bridge the gaps between the economic development and environmental protection were increasingly dominant. Empirically, many of the papers provided case studies of regions in China and other regions. The authorship reflected growing collaboration between researchers from many different countries or universities. While the great diversity of contributions on the topic reflected the wealth of insights generated on the topic in recent years, there is much more that must be done to achieve societal sustainability in natural resource management.No Full Tex

A data-driven approach design for carbon emission prediction of machining

The issue of carbon emission reduction for manufacturing industry attracts increasing attention. As a major contributor in the manufacturing industry, machining has generated large amounts of carbon emissions through the resource consumption, energy consumption, and waste disposal. The carbon emission prediction of machining is a priori technology for its reduction, and has been established as one of the most crucial research targets. The purpose of this study is to design a carbon emission prediction model of machining through a data-driven approach. First of all, the multiple sources and impact factors of carbon emissions in machining are studied, and the relationship between these factors is also studied to describe the carbon emissions. Then, a data-driven approach is designed to predict the carbon emission of machining, which consists of data collection and preprocessing, feature extraction, prediction model establishment and model validation. The ridge regression, BP neural network based on Genetic Algorithm (GA-BP), root means square error (RMSE) and mean relative percentage error (MPAE) are respectively employed to fulfill the above tasks in the design approach. Finally, an experimental study of a real turning machining is proposed to verify the feasibility and merits of the designed approach

Sustainable resource allocation for power generation: The role of big data in enabling interindustry architectural innovation

Economic, social and environmental requirements make planning for a sustainable electricity generation mix a demanding endeavour. Technological innovation offers a range of renewable generation and energy management options which require fine tuning and accurate control to be successful, which calls for the use of large-scale, detailed datasets. In this paper, we focus on the UK and use Multi-Criteria Decision Making (MCDM) to evaluate electricity generation options against technical, environmental and social criteria. Data incompleteness and redundancy, usual in large-scale datasets, as well as expert opinion ambiguity are dealt with using a comprehensive grey TOPSIS model. We used evaluation scores to develop a multi-objective optimization model to maximize the technical, environmental and social utility of the electricity generation mix and to enable a larger role for innovative technologies. Demand uncertainty was handled with an interval range and we developed our problem with multi-objective grey linear programming (MOGLP). Solving the mathematical model provided us with the electricity generation mix for every 5 min of the period under study. Our results indicate that nuclear and renewable energy options, specifically wind, solar, and hydro, but not biomass energy, perform better against all criteria indicating that interindustry architectural innovation in the power generation mix is key to sustainable UK electricity production and supply