As the world’s largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO₂ and NO_x emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China’s continuous emission monitoring systems (CEMS) network during 2015–2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China’s industrial emission sources, which can offer insights into associated scientific studies and future policymaking.

To balance tourism’s economic benefit and environmental pollution, this paper proposes an analytical approach by using the input–output (IO) model and tourism satellite accounts (TSA). Four steps are taken: (1) the setting of system boundaries according to the combined IO and TSA database; (2) economic benefit estimation for tourism income, sectoral multipliers and inter-sector linkages; (3) environmental pollution estimation of direct and indirect CO₂ emissions; and (4) a policy analysis to balance the economic benefit and CO₂ emissions (in terms of reducing the CO₂ emissions intensity) in tourism-related sectors. In the case of Beijing, some interesting insights can be obtained. Beijing’s tourism sectors experienced a fast economic growth and a clear decrease in CO₂ emissions during 2007–2012, with the former having a greater absolute change rate (particularly for the shopping and sightseeing sectors). In all tourism sectors (except for transportation), the indirect CO₂ emissions were over three times greater than the direct CO₂ emissions. Transportation was a leading contributor to both the economic benefit (representing 91.65% of tourism income in 2012) and to environmental pollution (representing 38.75% of tourism-related CO₂ emissions). The detailed findings regarding the industrial and energy structures offer insightful policies for a high-benefit and low-emissions development of tourism.

As helpful big data, search engine data (SED) regarding tourism-related factors have currently been introduced to tourist volume prediction, but they have been shown to impact the tourism market on different timescales (or frequency band). This study develops a novel forecasting method using an emerging multiscale analysis—bivariate empirical mode decomposition (BEMD)—to investigate multiscale relationships. Three major steps are performed: (1) SED process to construct an informative index from sufficient SED using statistical analyses, (2) multiscale analysis to extract scale-aligned common factors from the bivariate data of tourist volumes and SED using BEMD, and (3) tourist volume prediction using an SED-based index. In the empirical study, the novel BEMD-based method with SED is used to forecast the tourist volume of Hainan in China, a global tourist attraction, and significantly outperforms both popular techniques (not considering SED or multiscales) and similar variants (considering SED or multiscales) in accuracy and robustness.

The gradual adjustment of fertility and retirement policies in China has social benefits in terms of coping with population aging. However, the environmental consequences of these policies remain ambiguous. Here we compile environmentally extended multiregional input–output tables to estimate household carbon footprints for different population age groups in China. Subsequently, we estimate the age-sex-specific population under different fertility policies up to 2060 and assess the impacts of fertility and retirement policies on household carbon footprints. We find that Chinese young people have relatively higher household carbon footprints than their older counterparts due to differences in income by age group. Relaxing fertility policies and delaying retirement age are associated with an increase in population (and labour supply) and thus increases in household carbon footprints, with the majority of these increases from the fertility side. These results may help policymakers understand interactions among those measures targeting population aging and climate action.

Purpose: The purpose of this paper is to develop a multi-agent-based simulation model for the online opinion dissemination during hazardous chemical leakage emergencies into rivers in China, to explore an appropriate crisis information release policy of China’s government for controlling public panic. Design/methodology/approach: In the proposed model, two fundamental attributes of crisis information, i.e., truthfulness (for true or false news) and attitude (for positive, neutral or negative opinion), are considered. Four major agents in the online community system, i.e., citizens, the government, media and opinion leaders, are included. Using four typical accidents of hazardous chemical leakage into rivers in China as case studies, insightful policy implications can be obtained for crisis management and panic control. Findings: The news about the terrible potential damages from such a type of accidents will instantly arise wide-ranging public panic; therefore, the corresponding crisis information release policy should be carefully designed. It is strongly advised against publishing false news to temporarily conceal the accidents, which will seriously hurt the government’s reputation and agitate much larger-scale public panic in terms of degree and duration. To mitigate public panic, the true news especially about treatment measurements should be published immediately. If the government does nothing and releases no crisis information, the public panic will go out of control. Research limitations/implications: This paper only focuses on the crisis information release policies from the perspectives of the government. Furthermore, this study especially focuses on the cases in China, and extending the proposed model study for general contexts is an important direction to improve this study. Finally, the proposed model should be extended to other types of emergencies to further justify its generalization and universality, especially various natural catastrophes like storms, floods, tsunamis, etc. Originality/value: This paper develops a multi-agent-based model for online public opinion dissemination in emergency to explore an appropriate crisis information release policy for controlling public panic stemming from hazardous chemicals leakage accidents into rivers. The proposed model makes major contributions to the literature from two perspectives. First, the crisis information about emergency accidents are divided into true and false news based on the truthfulness attribute, and into neutral, positive and negative emotions based on the attitude attribute. Second, the proposed model covers the main agents in the online virtual community.