*1.1. Background and Status*

To fight against climate warming and environmental pollution, carbon peaking and carbon neutrality have become the main energy development strategies worldwide. At present, in China, various industries such as electric power, transportation, construction, steel, etc., have proposed different routes for achieving carbon peaking and carbon neutrality [1]. The carbon emission of electricity generation based on fossil fuel provides a larger proportion [2]. That is, reducing carbon emissions from electricity production processes is vital to achieving carbon neutrality for China. Recently, the primary solution is the improvement of energy utilization efficiency and increase of renewable energy generation. The rapid development of renewable energy, such as wind power and photovoltaic, has formed an integrated energy system with traditional coal-fired power generation and hydroelectric power generation, turning into China's main centralized energy supply [3].

For the development of the integrated energy system, many studies have explored carbon peaking and carbon-neutral pathways in energy use from technical, economic, and social perspectives [4–7]. For example, Liu et al. [8] proposed a differentiated model for evaluating the impact of the emissions trading scheme (ETS) on the development of non-fossil energy sources, and it is concluded that the ETS significantly promotes the development of non-fossil energy sources in China, and the higher the carbon price, the stronger the effect

**Citation:** Zhang, Y.; Wang, S.; Shao, W.; Hao, J. Feasible Distributed Energy Supply Options for Household Energy Use in China from a Carbon Neutral Perspective. *Int. J. Environ. Res. Public Health* **2021**, *18*, 12992. https://doi.org/10.3390/ ijerph182412992

Academic Editors: Roberto Alonso González Lezcano, Francesco Nocera and Rosa Giuseppina Caponetto

Received: 15 November 2021 Accepted: 7 December 2021 Published: 9 December 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

of ETS on promoting the development of non-fossil energy sources. Li et al. [9] developed a collaborative hierarchical framework to coordinate electricity and heat interactions and analyzed the impact of carbon tax, electricity and heat demand responses on the outcome of multi-stakeholder interaction problems. The results show a win-win situation for all participants, with significant reductions in total costs and CO2 emissions. Zhao et al. [10] investigated the technical and economic feasibility of New York State's energy transition goals. They developed an energy conversion optimization framework, pointed out that air-source heat pumps and geothermal technologies will provide 47% and 41% of heat demand, respectively, by 2050. Bao et al. [11] proposed a classification method for renewable energy-led distributed energy supply models. They provided an integrated economic and environmental evaluation model and concluded that the biomass waste-based supply model could achieve "zero" carbon emissions and "zero" energy consumption. In addition, it is advisable to promote waste-based energy utilization and wind and solar energy-based supply modes in new rural and remote areas with abundant resources.

Meanwhile, the distributed energy system has become an attractive alternative technology that has received much attention. Especially on the customer side, distributed energy supply systems can be used to increase the flexibility of the customer side and further improve the consumption and utilization of renewable energy [12–16]. For example, Huo et al. [17] innovatively developed an integrated dynamic simulation model and explored possible emission peaks and peak times by scenario analysis and Monte Carlo simulation methods. The dynamic sensitivity analysis shows that GDP per capita, carbon emission factor, and urban residential floor area play an essential role in driving carbon emissions' peak and peaking times. Zhang et al. [18] reviewed the distributed generation PV policy changes since 2013. They examined their impact on China's domestic distributed generation PV market, presented a cost and time breakdown for installing distributed generation PV projects in China, and identified the major barriers to distributed generation PV installation. Zhao et al. [19] calculated the internal rate of return (IRR) and static payback period for some distributed PV systems in five cities with different resource zones in China. The effects of relevant policy variables such as subsidies, benchmark prices, tariffs, and taxes on the economic performance of distributed power systems were discussed. Duan et al. [20] analyzed the influence of solar energy substitution for coal-fired power generation on future greenhouse gas emission trajectories and peak arrival times based on the full-spectrum and life-cycle perspective based on an integrated energy–economic– environment model and a simple climate response model. Moreover, from the application and evaluation perspective of the distributed energy system, Zeng et al. [21] described the current situation of distributed energy development in China. Sameti et al. [22] established the optimal design emissions of the district energy system based on a trade-off between annualized total cost and annual CO2 and showed that a district energy system with energy storage provides the best solution to environmental and economic problems. Ren et al. [23] studied the feasibility of distributed energy systems for three typical building clusters in one major city in each of China's five climate zones. Huang et al. [24] constructed a practical evaluation index system that integrates soft and hard competitiveness and classified distributed energy supply system scenarios according to development characteristics. Yoon et al. [25] artificially assessed the possibility of introducing cogeneration distributed energy systems in existing multi-family dwellings. The annual energy consumption of a typical urban multifamily dwelling was estimated based on primary energy consumption reduction, CO2 emission reduction, simple payback period, and recurring cost values.

The above existing research on distributed energy supply systems has been carried out from various aspects such as system energy source, process construction, operation strategy, economic evaluation, and application scenarios. Combined with carbon emission requirements, different distributed energy supply schemes are given from cities, industrial estates, buildings, etc. [26–28]. These studies provide the necessary basis for the development of distributed energy supply systems. Besides, household energy consumption playing a vital role as main energy consumption on the user side is also significant for

carbon peaking and carbon neutrality [29–32]. For example, Wu et al. [33] provided a systematical overview of rural household energy consumption in China from 1985 to 2013 and illustrated the pattern of rural household energy consumption using a comprehensive household survey, the Chinese Residential Energy Consumption Survey (CRECS, 2013). Zou et al. [34] presented a detailed analysis of rural household energy consumption characteristics based on the data of 1472 rural households from the Chinese General Social Survey of 2015 (CGSS2015). Ren et al. [35] predicted energy consumption and carbon emissions using both the carbon emissions coefficient and the sector energy consumption method and concluded that urbanization positively affects energy consumption and carbon emissions in China. Chen et al. [36] proposed a spatial downscaling framework to identify different provinces and sectors' roles in promoting carbon emission peaks. Zhang et al. [37] developed a questionnaire survey to investigate and evaluate the carbon emissions of household energy consumption and analyzed the influence factors, including residential consumption, housing conditions, daily travel distance, etc.
