This article introduces a new model that combines an ARIMA with a chaotic BP (Backforward Propagation Neural Network) algorithm for exchange rate forecasting purposes, which is based on sample data collected from January 4, 2010, to October 20, 2011. The forecast of the exchange rate trend is then provided for the subsequent twenty-five days. Other models are also constructed, such as the ARIMA, BP, ARIMA, and BP algorithms, in order to evaluate the forecast accuracy. Based on our results, the combination of an ARIMA and a chaotic BP algorithm outperforms all other models in terms of the statistical accuracy of short-term forecasts.

PurposeUnderstanding China's carbon dioxide (CO2) emission status is crucial for getting Carbon Neutrality status. The purpose of the paper is to calculate two possible scenarios for CO2 emission distribution and calculated input-output flows of CO2 emissions for every 31 China provinces for 2012, 2015 and 2017 years.Design/methodology/approachIn this study using the input and output (IO) table's data for the selected years, the authors found the volume of CO2 emissions per one Yuan of revenue for the industry in 2012 and the coefficient of emission reduction compared to 2012.FindingsResults show that in the industries with a huge volume of CO2 emissions, such as “Mining and washing of coal”, the authors cannot observe the reduction processes for years. Industries where emissions are being reduced are “Processing of petroleum, coking, nuclear fuel”, “Production and distribution of electric power and heat power”, “Agriculture, Forestry, Animal Husbandry and Fishery”. For the “construction” industry the situation with emissions did not change.Originality/value“Transport, storage, and postal services” and “Smelting and processing of metals” industries in China has the second place concerning emissions, but over the past period, emissions have been sufficiently reduced. “Construction” industry produces a lot of emissions, but this industry does not carry products characterized by large emissions from other industries. Authors can observe that Jiangsu produces a lot of CO2 emissions, but they do not take products characterized by significant emissions from other provinces. Shandong produces a lot of emissions and consumes many of products characterized by large emissions from other provinces. However, Shandong showed a reduction in CO2 emissions from 2012 to 2017.

The transition to a “green” economic model is a complex strategic task that requires a combination of two previously incompatible development vectors: maintaining dynamic economic growth and preserving the natural environment on a long-term basis. No country has yet been able to cope with such complexity, nevertheless, an active search for a new balanced model continues, with the development of appropriate strategies. China is among the countries moving in this direction. The article analyzes the influence of social, economic, and environmental factors on the prospects for the development of a green economy and the preservation of natural areas in China. The dynamics of changes in the ecological situation from 1970 to 2018 is investigated. The authors propose a methodology for assessing the state of the environment based on demographic dynamics, economic indicators, and the level of technological development. Over the past 50 years, China has experienced intensive industrial development, as a result of which the degradation of valuable natural assets is increasing in most regions. At the same time, efforts are being made in a number of provinces to remedy the situation through the formulation of new policies, the first results of which have been already visible. The government has established a new environmental legislation designed to scale the green practices of the pioneering regions throughout the country, including the trend toward the de-urbanization of individual megacities and others. The implementation of this strategy will be facilitated by the expansion of interdisciplinary scientific research, the development of complex technological solutions, and development programs that simultaneously take into account various factors.

This paper concerns the necessity of ecosystem protection and energy efficiency rating development. The article analyzes the experience of the non-commercial Environmental and Energy Rating Agency (Interfax-ERA) ratings concerning the environmental assessment of Russian regions and the transfer of successful knowledge for evaluating 31 Chinese provinces. The theoretical base, quantitative and qualitative characteristics of the energy-resource efficiency (ERE) rating, technological efficiency (TE), and ecosystem impact (EI) ratings are proposed based on the system methodology, developed within the framework of the UN Sustainable Development Goals (SDGs). The primary study objective is to determine whether the Interfax-ERA rating methodology and considered criteria could be applied in China to assess the provinces’ environmental, technological, and energy efficiency. The research highlights the importance of multifunctional tools for developing experiences and sharing methodological experiences across countries. The study efficiently emphasizes provinces with a high level of energy efficiency and technological innovations as well as the provinces with the deficient level of eco-oriented economy policy. The results show two types of systematic deviations—significantly high-level impact on the ecosystem in the Chinese provinces and considerably high levels of energy and resource efficiency in capitals and business centers.

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