Chunyan Wang’s research while affiliated with Tsinghua University and other places

The increasing amount of pressure related to water and energy shortages has increased the urgency of cultivating individual conservation behaviors. While the concept of nudging, i.e., providing usage-based feedback, has shown promise in encouraging conservation behaviors, its efficacy is often constrained by the lack of targeted and actionable content. This study investigates the impact of the use of large language models (LLMs) to provide tailored conservation suggestions for conservation intentions and their rationale. Through a survey experiment with 1,515 university participants, we compare three virtual nudging scenarios: no nudging, traditional nudging with usage statistics, and LLM-powered nudging with usage statistics and personalized conservation suggestions. The results of statistical analyses and causal forest modeling reveal that nudging led to an increase in conservation intentions among 86.9%-98.0% of the participants. LLM-powered nudging achieved a maximum increase of 18.0% in conservation intentions, surpassing traditional nudging by 88.6%. Furthermore, structural equation modeling results reveal that exposure to LLM-powered nudges enhances self-efficacy and outcome expectations while diminishing dependence on social norms, thereby increasing intrinsic motivation to conserve. These findings highlight the transformative potential of LLMs in promoting individual water and energy conservation, representing a new frontier in the design of sustainable behavioral interventions and resource management.

Classifying household water-consumption behaviors is crucial for providing targeted suggestions for water-saving behaviors and enabling effective resource management and conservation. Although it is common knowledge that energy consumption is closely coupled with household water consumption, the effectiveness of energy consumption information in classifying household water behaviors remains unexplored. This study proposes a hybrid model of long short-term memory (LSTM) and random forest (RF) using water and electricity consumption as inputs to classify household water-consumption behaviors. Data from three households in Beijing collected from January to March 2020 were used for the case studies. The hybrid model achieved a macro F1 score of 0.89 at a 5-min resolution, outperforming the standalone LSTM and RF models. Additionally, the inclusivity of time-series electricity consumption improves the accuracy (F1 scores) of classifying bathing and laundry behaviors by 0.12 and 0.20, respectively. These findings underscore the scientific value of integrating electricity consumption as a proxy variable in water-consumption behavior classification models, demonstrating its potential to enhance accuracy while simplifying data acquisition processes. This study establishes a framework for demand-side water management aimed at empowering residents to understand their own water-energy consumption behavior patterns and engage in personalized water conservation efforts.

The Water-Energy Nexus (WEN) provides a comprehensive concept for the cooperative management of resources. Although the WEN system in cities is intricately connected to socioeconomic activities, relationship between WEN and economic systems remains understudied. This study introduces a tri-dimensional Nexus Pressure Index (NPI) to assess the pressure on WEN system. Gross Domestic Product (GDP) per capita and city tiers in the urban agglomeration were used to assess the relationship between the characteristics of WEN and economic system. We conducted a case study of 296 cities in China and 1330 counties in the United States from 2012 to 2019. During the 9 year study period, on average, pressure on WEN system have relieved by 22% in China and 27% in the United States, measured by NPI. Cities with most ideal characteristics (low pressure in all dimensions) rank merely in the middle of all eight classes, with GDP per capita 74% and 85% of the highest-GDP-per-capita class in China and the US respectively. Well-performing WEN system does not yield best economic outcomes. High water pressure correlates with better economic performance in the US, while high-energy-pressure cities had GDP per capita about 50% and 70% of the class with highest GDP per capita in China and the US, respectively, suggesting stronger economic constraints from energy stress. Urban agglomeration analysis revealed a negative relationship between WEN and economic performance. NPI in emerging cities is 0.6–1 lower than NPI in regionally-central cities in China, while 0.2–0.5 lower in the US. These results underscore the contradiction between preferred WEN characteristics and higher economic performance, and underpin the resource curse hypothesis at city-level in the two considered giants. A sustainable approach to harmonize WEN and economic system is in urgent need.

Chlorine-based disinfectants (CBDs) have been widely used to prevent and control the spread of the COVID-19, which may lead to the formation of carcinogenic hazards. In China, strict disinfection strategies by local governments/communities or volunteering by residents have been implemented to meet the Dynamic COVID Zero (DCZ) Strategy. However, the amount of CBDs used has not been estimated. The author proposed an urban-scale disinfectant consumption estimation (ALICE) model to quantify weekly CBD consumption. The results show that the CBD consumption for the urban region of Beijing during the DCZ strategy was 3704.0 t (0.43 kg/(cap·yr)), equivalent to a monthly increase of 15 g/cap (70.5%) in CBD consumption compared with that in pre-pandemic. According to the scenario analysis, a stricter strategy with a shorter response time toward new cases will decrease the total CBD consumption by 1.2% compared with the baseline estimation. A more precise prevention strategy with a smaller delineation of risk area and a less stringent strategy with a longer response time will lower the total CBD consumption by 0.42% and 0.35%, respectively. Specifically, the more precise prevention strategy will reduce CBD consumption of close off and lockdown area (COLD area) by 16.9%, and the stricter strategy will reduce this consumption by 37.7%. This study highlights the impact of pandemic prevention and control strategies on chlorine-based disinfectant consumption and some implications for future environmental pollution and risk assessments.

Estimating household water consumption can facilitate infrastructure management and municipal planning. The relatively low explanatory power of household water consumption, although it has been extensively explored based on various techniques and assumptions regarding influencing features, has the potential to be enhanced based on the water-energy nexus concept. This study attempts to explain household water consumption by establishing estimation models, incorporating energy-related features as inputs and providing strong evidence of the need to consider the water-energy nexus to explain water consumption. Traditional statistical (OLS) and machine learning techniques (random forest and XGBoost) are employed using a sample of 1320 households in Beijing, China. The results demonstrate that the inclusion of energy-related features increases the coefficient of determination (R2) by 34.0% on average. XGBoost performs the best among the three techniques. Energy-related features exhibit higher explanatory power and importance than water-related features. These findings provide a feasible modelling basis and can help better understand the household water-energy nexus.

The United States is the world's second‐largest producer and consumer of plastics and the largest producer of plastic waste. Understanding the sources, drivers, and destinations of plastic production, consumption, and waste is critical for the United States to develop strategies toward a zero‐plastic pollution future. Here, we characterize the dynamic material flows and stocks of plastics in the United States for nearly seven decades (1950–2018) and project the future trajectories until 2050 under various scenarios on the basis of reduce, reuse, and recycle to explore pathways toward zero plastic pollution. Our estimation shows that 1479 MMt plastics were produced in the United States from 1950 to 2018, 75 MMt waste plastics were domestically recycled, 139 MMt virgin polymers were exported, and 9 MMt recycled waste plastics were imported. Currently, about 326 MMt of plastics still remain in the society as in‐use stock, most of which (63%) are in the construction sector. Plastic pollution would almost double from 37 MMt in 2018 to 86 MMt in 2050 if current consumption pattern and waste management remain unchanged. Single strategies (i.e., plastic bag ban and extended lifespan) could only contribute limited reductions (2%–12%) of plastics pollution, and would not be able to reverse the increasing trajectory of plastic pollution until 2050. Additional measures are needed, such as improving recycling and avoiding landfilling of plastic waste. Our analysis can provide critical insights to help the United States develop long‐term strategies to mitigate and eliminate plastic pollution.

To improve energy-saving management, the energy efficiency grade (EEG) was introduced by the Chinese government in the 2000s and mainly implemented for white goods (WGs) in early stages. However, due to the lack of actual statistics, how effective the promotion of high EEG WGs has been in China is still not clear. The China Energy Efficiency Grade (CEEG) of WGs dataset described here comprises (i) EEG-related data on 5 kinds of WGs at the regional (national, provincial) and household levels in China and (ii) predictions of future average EEG trends. By web crawling, retrieving and processing in SQL, the average EEG data weighted by sales in 30 provinces in mainland China from 2012 to 2019 are provided. Household WG survey data, including household information and average EEG, were collected by distributing questionnaires to 1327 households in Beijing, China. The CEEG dataset will facilitate the advancement of research on household energy consumption, household appliance consumer choice, and the assessment of energy efficiency-related policies.