The speedy raise of residential buildings' carbon emissions is a hindrance to achieving China's 2030 carbon peak goal. This study constructs an assessment framework for comprehensive consideration of 30 Chinese provinces' socioeconomic circumstances, energy demand, and emissions reduction technology to meet the consistent coupling degree of equity and efficiency (CDEE). This study is the first to propose an allocation scheme for equilibrating provincial carbon increments for rural and urban residential buildings in 2030 under carbon peaking constraints. The relevant results are fourfold. (1) Residential building's floor area per capita and energy carbon emissions coefficients are the soliddest drivers to facilitate and inhibit the raise of carbon emissions during 2010–2020. (2) Through dynamic Monte Carlo simulation from 2021 to 2030, we demonstrate that provinces with the most gamey carbon emissions in urban and rural areas include Shandong, at 121.52 (± 5.50) Mt. and Hebei, at 61.34 (± 3.08) Mt. in 2030, respectively. (3) A CDEE of 52.3% (biased equity) in urban areas and 34.5% (biased efficiency) in rural areas indicates equilibrated allocation of provincial carbon increment. (4) In the final 2030 allocation scheme, the greatest carbon mitigation pressures are in Beijing (11.34 Mt) and Heilongjiang (3.23 Mt), and the provinces with the largest carbon increment in urban areas include Hebei, Henan, and Guangdong, while the largest carbon increments in rural areas are in Hebei, Henan, and Guangdong. Overall, this study furnishes a targeted and valuable decision making reference for the government to determine provincial carbon peak goals for Chinese residential buildings.
Deep neural networks can be employed for estimating the direction of arrival (DOA) of individual sound sources from audio signals. Existing methods mostly focus on estimating the DOA of each source on individual frames, without utilizing the motion information of the sources. This paper proposes a method for estimating trajectories of sources, leveraging the differential of trajectories across different time scales. Additionally, a neural network is employed for enhancing the trajectories wrongly estimated especially for sound sources with low-energy. Experimental evaluations conducted on simulated dataset validate that the proposed method achieves more precise localization and tracking performance and encounters less interference when the sound source energy is low.
Chlorinated paraffins (CPs), mainly short-chain CPs (SCCPs) and medium-chain CPs (MCCPs), are currently the most produced and used industrial chemicals related to persistent organic pollutants (POPs) globally. These chemicals are widely detected in the environment and in the human body. As the release of SCCPs and MCCPs from products represents only a small fraction of their stock in products, the potential long-term release of CPs from a large variety of products at the waste stage has become an issue of great concern. The results of this study showed that, by 2050, SCCPs and MCCPs used between 2000 and 2021 will cumulatively generate 226.49 Mt of CP-containing wastes, comprising 8610.13 kt of SCCPs and MCCPs. Approximately 79.72 Mt of CP-containing wastes is predicted to be generated abroad through the international trade of products using SCCPs and MCCPs. The magnitude, distribution, and growth of CP-containing wastes subject to environmentally sound disposal will depend largely on the relevant provisions of the Stockholm and Basel Conventions and the forthcoming global plastic treaty. According to multiple scenarios synthesizing the provisions of the three conventions, 26.6–101.1 Mt of CP-containing wastes will be subject to environmentally sound disposal as POP wastes, which would pose a great challenge to the waste disposal capacity of China, as well as for countries importing CP-containing products. The additional 5-year exemption period for MCCPs is expected to see an additional 10 Mt of CP-containing wastes subject to environmentally sound disposal. Thus, there is an urgent need to strengthen the Stockholm and Basel Conventions and the global plastic treaty.
This paper aims to examine the impact of the digital economy on urban entrepreneurship and its spatial spillover effects. To achieve this purpose, this research relies on data from 252 prefecture-level cities in China from 2012 to 2019. The findings demonstrate that the development of the digital economy has a positive influence on entrepreneurial activity in cities, with particularly effects observed robust at higher quantile levels. Additionally, the results suggest that urban entrepreneurial activity may be a siphoning effect, impeding entrepreneurship in neighboring cities. Furthermore, further investigation shows regional and policy heterogeneity.
Promoting rural family entrepreneurship is an effective way to realize rural revitalization. The primary aim of this study is to assess the entrepreneurial impact of family social capital on rural households in China. The objective of this study is to understand how family social capital affects rural entrepreneurship in a Chinese context. Using data from the 2020 China Family Panel Studies, this study empirically tests the effect of family social capital on rural family entrepreneurship. Research shows that family social capital is significantly and positively correlated with rural family entrepreneurship, indicating that it is an essential determinant in promoting rural family entrepreneurship. Internet use is an effective transmission path for family social capital, which affects rural entrepreneurship, and the impact of rural entrepreneurship varies with family size and household head characteristics. This study not only enriches the theoretical understanding of rural entrepreneurship but also sheds light on the behavioral mechanisms that explain the entrepreneurial process of rural households. To promote rural entrepreneurship and revitalization, it is important to be adept at activating family social capital.
Injecting CO2 when the gas reservoir of tight sandstone is depleted can achieve the dual purposes of greenhouse gas storage and enhanced gas recovery (CS-EGR). To evaluate the feasibility of CO2 injection to enhance gas recovery and understand the production mechanism, a numerical simulation model of CS-EGR in multi-stage fracturing horizontal wells is established. The behavior of gas production and CO2 sequestration is then analyzed through numerical simulation, and the impact of fracture parameters on production performance is examined. Simulation results show that the production rate increases significantly and a large amount of CO2 is stored in the reservoir, proving the technical potential. However, hydraulic fractures accelerate CO2 breakthrough, resulting in lower gas recovery and lower CO2 storage than in gas reservoirs without fracturing. Increasing the length of hydraulic fractures can significantly increase CH4 production, but CO2 breakthrough will advance. Staggered and spaced perforation of hydraulic fractures in injection wells and production wells changes the fluid flow path, which can delay CO2 breakthrough and benefit production efficiency. The fracture network of massive hydraulic fracturing has a positive effect on the CS-EGR. As a result, CH4 production, gas recovery, and CO2 storage increase with the increase in stimulated reservoir volume.