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In this study we suggest that a more careful and systematic understanding of fuel poverty can be developed through a multidimensional approach to the relationship between monetary poverty, residential energy efficiency, and heating restriction. Our objective is to provide new ways to better identify those who suffer the most from fuel poverty to optimize policy. Thus, the purpose of this paper is to measure poverty in three steps following Sen (1979): (i) combining poverty characteristics into an aggregate measure involving a fuel poverty index (FPI), (ii) identification and comparison of poor people according to existing and new definitions and (iii) testing the robustness of the fuel poverty composite indicator. Our results show that the usual measures reveal a gap that does not consider all the dimensions of fuel poverty, excluding those who are at or above a certain threshold, but who are nevertheless vulnerable.

Better understanding of households’ fuel type choice behaviour for residential heating, cooking, or lighting purposes would provide valuable information in estimating households’ energy use and in developing efficient fuel switching and energy saving policies; these solutions could include reducing consumption and utilising renewable energy sources. This paper aims to explore potential determinants of household’s fuel choice for residential heating in Türkiye. Using nineteenth wave of Household Budget Survey which was administered to 11,828 households and 40,688 individuals throughout the country, the data were analysed using both multinomial logit (MNL) and multinomial probit (MNP) models due to unordered nature of the dependent variable category. The empirical findings indicate household type, type of dwelling, residence time, the age of dwelling, the number of rooms, housing size, household size, type of floor structure of dwelling, household annual disposable income (log), household head’s occupation level, type of heating system, car ownership, and type of employment were found as statistically significant factors affecting Turkish household’s fuel choice for residential heating. Household annual disposable income and type of heating system had the highest impact on household’s final fuel type decision. Results also reveal that MNL is more parsimonious model than MNP model.

The article proposes a model for the functioning of the district heating industry under the conditions of the alternative boiler house mechanism. This model is used to build forecast scenarios for the development of the industry and its two segments: the alt boiler room and other district heating systems. In addition, options for reforming the industry are shown and mechanisms and tools are proposed for its transition to break-even operation.

This study explores the factors that impact residential energy usage and spending in the United States. Using data from the 2020 Residential Energy Consumption Survey (RECS), we investigate the significance of different energy consumption determinants at various analysis levels. Our analysis covers residential energy usage, electricity, natural gas, propane, and fuel oil consumption. We also examine energy usage for space heating, cooling, and water heating. To leverage the extensive RECS data, which includes over 180 variables, we utilized machine learning (ML) techniques for feature selection and determined their Shapley contribution for different target outcomes. Our results indicate that the CatBoost algorithm outperforms other ML techniques on the 2020 Residential Energy Consumption Survey sample. Our findings demonstrate that it is not appropriate to aggregate consumption and expenditure, as each level has distinct important features. JEL Classification: D12, Q41, R21

One of the areas for increasing energy efficiency in the production of electrical and thermal energy is the use of cogeneration units (CGU), which is due to an increase in the share of useful heat output to heat supply systems. Large combined heat and power plants (CHPs), as a rule, use steam turbine units, which serve as sources of thermal energy for heating and hot water supply in cities with a high concentration of heat load. In small and medium-sized cities, the main sources of thermal energy are boiler houses. With the advent of modern gas piston and gas turbine units on the market, it is possible to predict the creation of mini-CHPs based on them. To increase their fuel efficiency, it is advisable to use seasonal thermal storage (TS) as part of the municipal energy infrastructure in order to reduce the volume of thermal emissions during the nonheating period.

In Krasnoyarsk, where the main type of fuel is coal, there is an acute problem of air pollution from industrial and mobile sources aggravated by the hollow landscape and decreasing average annual wind speed. One of the possible solutions to this problem is gas infrastructure development, which, according to the latest official publications, is to be implemented by 2028. The article assesses the cost and feasibility of several options of gas supply systems as well as alternative ways to reduce pollutant emissions through the use of other fuels. Based on this analysis, short-term and long-term solutions to the city’s environmental problems are proposed.

Today, green energy is a new key feature of the energy sectors in developed and dynamically developing economies. It can stimulate the development of small and medium-sized businesses, greatly impacts on nature conservation and generates a strong technological impulse. Canada is known as one of the countries with a highly developed green energy and is considered to use most of its resources efficiently. Russia possesses significant reserves of natural resources and has high potential in the field of green energy production; however, the share of green energy in its energy balance is not high. The authors have compared Canada and Russia in terms of climatic conditions, financial situation, potential for green energy and social attitudes towards green energy, and demonstrated the similarity of the two countries. Having conducted a regression analysis of green energy production, the authors have further analyzed the regional potential for green energy development by comparing energy prices and proved that green energy is cheaper in Canada. Based on the obtained results, the authors have developed a system of recommendations for Russia in the field of green electricity and for both countries in the field of green heating. The main findings include the system of recommendations, the reasons for the reluctance of Russian society to introduce green energy, and a regional analysis of the most suitable regions for green transformation.

This paper investigates the activity of a multi-utility that uses Waste-to-Energy (WtE) and cogeneration (COG) plants to provide services in the heat and in the electricity markets. We assume that it employs WtE and COG units to participate in the day-ahead and real-time electricity markets and, with the support of heat-only units, it satisfies the heat demand of local district heating areas (DHAs). We use stochastic programming to develop three linked problems that describe the sequence of the decision process regarding the operation of WtE and COG facilities: (i) the first problem considers the point of view of the multi-utility that defines the day-ahead and the real-time heat scheduling of its plants and the maximum amount of electricity that WtE and COG units can offer in the day-ahead electricity market, taking into account the uncertainty of the real-time heat demand; (ii) the second problem models the day-ahead electricity market cleared by the Power Exchange, where the electricity dispatch of WtE and COG units is limited by the maximum power offers defined in step (i); and (iii) given the heat and power schedules respectively determined in steps (i) and (ii), the last problem describes the activity of theTransmission System Operator that re-dispatches WtE and COG outputs and decides their participation in the reserves and the real-time electricity markets, incorporating uncertain electricity demand and renewable power generation. Italian data are taken to investigate the operation of the WtE and COG plants under different assumptions. A reference analysis shows that these facilities obtain stable revenues from the heating market, but those from the electricity markets are very variable and mainly derives from reserve procurement. In addition, we perform two sensitivity analyses: in the first one, where we consider high natural gas and CO2 prices, the use of WtE and COG units increases to substitute gas-fueled plants with a consequent increase of their profits; in the second one, which describes an electricity market with a high renewable penetration, the activity of these facilities becomes more irregular because of the augmented penetration of intermitted renewable generation.

The Huai River policy divides the country into the areas with and without central heating. This paper first determines the location of the south-north central heating borderline in China based on the Huai River policy, and then studies the differences in population migration between the north and the south. It proposes three factors that influence people’s decision to migrate through district heating: heating costs, indoor living conditions, and outdoor air quality. Through the analysis of the county-level data in China, the following three basic conclusions are drawn: first, based on the estimation of the total sample, the total effect of central heating on population in-migration is negative; Second, central heating positively impacts in-migration in wealthier counties, while the impact is negative in less-developed counties. Third, the heating boundary policy affects population migration via air pollution. However, air pollution is not the decisive factor.

In this study, a TRNSYS model for a solar‐assisted heat pump (SAHP) water heating system for a university bathroom in Xi'an was developed and validated through experimental measurements. Under the typical meteorological conditions in Xi'an, a comparative analysis of the performance of the serial indirect expansion SAHP (IDX‐SAHP) system and the parallel IDX‐SAHP system during different seasons was conducted using the validated model. The results demonstrated that for annual operation, the parallel IDX‐SAHP system showed better performance in the Xi'an district compared to the serial system. Under the same heating load, the average coefficient of performance (COP) of the serial IDX‐SAHP system was 3.23 and that of the parallel IDX‐SAHP system was 4.34. In particular, during summer and the transition season, the parallel IDX‐SAHP system showed large advantages, mainly owing to a larger part of the heating load being provided by the solar collector. However, the serial IDX‐SAHP system showed a higher efficiency than that of the parallel IDX‐SAHP system during winter conditions. The validated simulation models for the serial and parallel solar‐assisted heat pump heating systems were developed. By the proposed models, performances of the serial and parallel systems under different weather conditions in Xi'an area were illustrated. The results in this work could provide a reference for the practical design of solar‐assisted heat pump heating systems in Xi'an, China.