Digital monitoring technology and air quality: Evidence from the People's Republic of China

Liang, Pinghan; Liu, Yadi; Tian, Shu Working Paper Digital monitoring technology and air quality: Evidence from the People's Republic of China ADB Economics Working Paper Series, No. 788 Provided in Cooperation with: Asian Development Bank (ADB), Manila Suggested Citation: Liang, Pinghan; Liu, Yadi; Tian, Shu (2025) : Digital monitoring technology and air quality: Evidence from the People's Republic of China, ADB Economics Working Paper Series, No. 788, Asian Development Bank (ADB), Manila, https://doi.org/10.22617/WPS250251-2 This Version is available at: https://hdl.handle.net/10419/322388 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/3.0/igo/ ASIAN DEVELOPMENT BANK ASIAN DEVELOPMENT BANK 6 ADB Avenue, Mandaluyong City 1550 Metro Manila, Philippines www.adb.org ADB ECONOMICS WORKING PAPER SERIES NO. 788 July 2025 Digital Monitoring Technology and Air Quality Evidence from the People’s Republic of China This paper empirically investigates the impact of digital environmental monitoring technologies on air quality in the People’s Republic of China using public procurement data. The findings show that following the adoption of digital environmental monitoring technology: (i) city-level PM2.5 concentrations exhibit a sizeable reduction, (ii) regulatory enforcement improves and heavily polluting firms exit more, (iii) green innovation is enhanced through more accurate pollutant identification, and (iv) the effectiveness of these technologies depends on the extent of information disclosure and public participation. About the Asian Development Bank ADB is a leading multilateral development bank supporting inclusive, resilient, and sustainable growth across Asia and the Pacific. Working with its members and partners to solve complex challenges together, ADB harnesses innovative financial tools and strategic partnerships to transform lives, build quality infrastructure, and safeguard our planet. Founded in 1966, ADB is owned by 69 members—50 from the region. DIGITAL MONITORING TECHNOLOGY AND AIR QUALITY EVIDENCE FROM THE PEOPLE’S REPUBLIC OF CHINA Pinghan Liang, Yadi Liu, and Shu Tian ASIAN DEVELOPMENT BANK The ADB Economics Working Paper Series presents research in progress to elicit comments and encourage debate on development issues in Asia and the Pacific. The views expressed are those of the authors and do not necessarily reflect the views and policies of ADB or its Board of Governors or the governments they represent. ADB Economics Working Paper Series Pinghan Liang, Yadi Liu, and Shu Tian No. 788 | July 2025 Pinghan Liang ([email protected]) and Yadi Liu ([email protected]) are professors at the School of Government, Sun Yat-sen University. Shu Tian ([email protected]g) is a principal economist at the Economic Research and Development Impact Department, Asian Development Bank. Digital Monitoring Technology and Air Quality: Evidence from the People’s Republic of China Creative Commons Attribution 3.0 IGO license (CC BY 3.0 IGO) © 2025 Asian Development Bank 6 ADB Avenue, Mandaluyong City, 1550 Metro Manila, Philippines Tel +63 2 8632 4444; Fax +63 2 8636 2444 www.adb.org Some rights reserved. Published in 2025. ISSN 2313-6537 (print), 2313-6545 (PDF) Publication Stock No. WPS250251-2 DOI: http://dx.doi.org/10.22617/WPS250251-2 The views expressed in this publication are those of the authors and do not necessarily reflect the views and policies ofthe Asian Development Bank (ADB) or its Board of Governors or the governments they represent. ADB does not guarantee the accuracy of the data included in this publication and accepts no responsibility for any consequence of their use. 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If the material is attributed toanother source, please contact the copyright owner or publisher of that source for permission to reproduce it. ADB cannot be held liable for any claims that arise as a result of your use of the material. Please contact [email protected] if you have questions or comments with respect to content, or if you wish toobtain copyright permission for your intended use that does not fall within these terms, or for permission to use theADB logo. Corrigenda to ADB publications may be found at http://www.adb.org/publications/corrigenda. Notes: In this publication, “$” refers to United States dollars. ADB recognizes “China” as the People’s Republic of China. ABSTRACT Since the early 21st century, air quality concerns in the People’s Republic of China (PRC) have garnered significant attention from both the public authorities and society. This study investigates the effects of digital environmental monitoring technology on air pollution. Specifically, we explore the data from government procurement of digital environmental monitoring technologies over the 2014–2019 period. The baseline results indicate that on average, each additional environmental contract per 100,000 residents signed by governments is associated with an 8-percentage point reduction in city PM2.5 levels. This effect arises from more accurate pollutant identification, which strengthens enforcement of environmental regulations, facilitating any necessary transition and, where applicable, orderly exit of heavily polluting enterprises, and fosters green innovation. Further, this effect exhibits regional variation in the extent of environmental concern and the level of information disclosure. The results suggest that technology-driven environmental governance, supported by public engagement and policy frameworks, plays a crucial role in enhancing air quality in the PRC. Keywords: digital environmental monitoring, PM2.5, public monitoring, information disclosure JEL codes: O18, O33, Q50 _______________________ This study was prepared as a background material for Harnessing Digital Transformation for Good: Asian Development Policy Report and funded by the Republic of Korea e-Asia and Knowledge Partnership Fund TA 6920 (Promoting Digitalization for Green and Inclusive Growth in Asia). 1. INTRODUCTION There is increasing interest from the public, city administrators, and regulators in air quality, as safe, clean air is essential for cities and communities to thrive (Smith and Doe 2020). In 2010, the People’s Republic of China (PRC) became the world’s second-largest economy; however, this growth came at a significant environmental cost. Regions such as Beijing, Tianjin, and Hebei Province have frequently suffered from severe smog, with air pollution levels reaching hazardous levels. These alarming indicators have prompted the PRC government to continuously prioritize environmental issues, implementing a series of policy reforms and technological advancements aimed at environmental improvement. In 2013, the PRC government announced an investment of CNY1.7 trillion ($277 billion) over the next 5 years to combat the dangerous and rapidly worsening air pollution resulting from the PRC’s rapid economic growth (Wu 2013). This initiative includes actively leveraging digital technologies to enhance environmental governance efficiency, as demonstrated by the widespread implementation of digital environmental monitoring systems in urban areas (Schlæger and Zhou 2019). Digital technology offers unique advantages in predicting pollution level changes and tracking dispersion pathways. This technology addresses the principal-agent information asymmetry between the government and polluters. By providing real-time, transparent data, it reduces uncertainty and effectively lowers pollution levels. Despite the numerous advantages of digital monitoring technology and its growing role in environmental governance in the PRC, the impact of digital monitoring technology on air pollution management remains insufficiently understood and warrants further investigation. We obtained environmental monitoring procurement contracts from the Chinese Government Procurement Network for the years 2014–2019, matched them with cities based on the addresses of the purchasing parties to examine the impact of government digital monitoring technology on city PM2.5 levels. The baseline regression results indicate that, on average, each additional environmental contract per 100,000 residents corresponds to an estimated 8% 2 reduction in PM2.5 levels. This effect arises from more accurate pollutant identification, which strengthens governmental enforcement of environmental regulations, expedites the exit of heavily polluting enterprises, and fosters green innovation. First, our research directly broadens and supplements discussions on environmental monitoring technology within the context of the PRC’s digitalization (Jin et al. 2020, Schlæge and Zhou 2019, Hao 2018), emphasizing the significance of technology-aided decision-making. Traditional environmental governance frameworks, such as market-based regulations, have positively impacted environmental governance (Mao and Wang 2016, Zhang et al. 2019). However, they often exhibit temporal delays. Our findings indicate that digital monitoring technology significantly enhances the efficiency of air pollution regulation, with its impact strongly correlated to the density of monitoring devices—a relationship confirmed within industrial parks (Huang et al. 2019). We extend this conclusion to city level, finding that higher levels of digital monitoring infrastructure effectively reduce overall air pollution. This finding carries substantial implications for other facets of the PRC’s environmental governance, suggesting that intensified monitoring efforts can support broader environmental improvement goals. Second, some studies have discussed the impact of environmental monitoring stations under central government regulation on air quality. On the one hand, monitoring stations have improved air quality, but this effect appears to be localized (Yang et al. 2024; He et al. 2020). On the other hand, environmental monitoring stations signify a shift in environmental monitoring authority, and the increase in digitalization and information disclosure reduces the potential for local data manipulation (Greenstone et al. 2022). Reported air pollution concentrations showed an immediate increase following the establishment of monitoring stations. This also motivated local government officials to address environmental pollution. No studies, to the best of our knowledge, have explored how local governments react to urban air quality when data regulation authority is transferred upwards. This issue is critical because increased information disclosure not only elevates public and government awareness of environmental pollution but also forces 3 local governments to adopt more efficient pollution control measures in response to public demands. Consequently, the expansion of the air monitoring network through the procurement of advanced digital monitoring technologies becomes a key strategy for local governments. Our study reveals that cities with higher levels of environmental information disclosure experience a significantly greater reduction in air pollution because of the impact of digital monitoring. Lastly, our research underscores that technology, while offering numerous governance benefits, cannot independently resolve complex environmental issues. Digital monitoring technology serves primarily as an information provider in air pollution management; however, its effectiveness depends on integration within a broader systemic governance framework. On the one hand, Greenstone et al. (2022) indicate an immediate and lasting increase of reported air pollution level after the introduction of automated monitoring of air quality. Yang et al. (2024) show that local governments’ strategic response to the automated monitors may limit the enforcement of environmental regulation. On the other hand, Buntaine et al. (2024) show that citizen participation via social media could improve use of the real-time emission information and strengthen environmental governance. Hence, tangible governance outcomes are achieved only when environmental issues garner substantial attention from both governments and the public. This indicates that, although technology supplies essential tools and platforms for governance, meaningful change arises through collaborative engagement between technology and key social forces, including government, industry, and the public. The structure of this report is as follows: Section 2 reviews the relevant background; Section 3 details the methodologies employed in this study; Section 4 presents the findings and discussion; and Section 5 concludes with a summary of key insights and policy recommendations. 4 2. INSTITUTIONAL BACKGROUND AND DEVELOPMENT OF ENVIRONMENTAL MONITORING TECHNOLOGY 2.1 Air Pollution and Digital Transformation Air pollution, a critical global environmental challenge, particularly affects developing countries, disrupting the Earth’s temperature balance, diminishing biodiversity, and exacerbating respiratory diseases (Chakraborty and Lee 2019；Syed, Folz, and Ali 2023)，and potentially reducing labor productivity (He, Liu, and Salvo 2019) .In recent years, the PRC has taken active measures to reduce air pollution and has made notable progress (Fig.1), from the perspective of PM2.5concentrations, there has been a sustained annual reduction in overall pollution levels across the CPPRR, indicating a trend of improvement in air quality over time. The government initiated this effort in 2013 with the “Air Pollution Prevention and Control Action Plan,” which launched a nationwide campaign to tackle air pollution and emphasized the use of digital monitoring methods to strengthen air quality supervision. In the era of smart cities, digital technology offers new opportunities for environmental governance. In fields such as traffic management and public safety, digital transformation has shown positive results (Westerman et al. 2014；Hollis 2019, Ardabili et al. 2023). Technologies such as big data, cloud computing, internet of things (IoT), and artificial intelligence (AI), with their real-time data collection, powerful data processing, and predictive capabilities, can analyze data patterns in conjunction with variables such as weather conditions, traffic density, and industrial emissions, enabling accurate pollution forecasting and preemptive policy measures, better respond to rapidly changing environmental conditions and policy needs (Shen 2018, Aggestam and Mangalagiu 2020). Currently, environmental monitoring technology has been widely applied in soil detection (Wilson 2012), water quality testing (Chen et al. 2018), and air quality monitoring (Ma et al. 2014, Van et al. 2015). Empirical evidence from Western countries shows that environmental monitoring and enforcement activities provide a significant deterrent effect, reducing both violations and emissions (Gray and Shimshack 2011). 11 Variable names N Mean Std.Dev. Min Max Information Disclosure (PITI) 589 3.89 0.30 2.74 4.41 Low-carbon city pilot (dummy) 1,398 0.42 0.49 0.00 1.00 Carbon emission trading pilot (dummy) 1,398 0.15 0.36 0.00 1.00 Joint Green Patents 1,130 2.67 3.80 1 15 Source: Authors’ calculations. 4. RESULTS 4.1 Baseline Results Table 2 presents the baseline regression results. In Column (1), we do not control for fixed effects or other influencing factors, and the regression coefficient indicates that an additional environmental monitoring procurement contract per 100,000 people corresponds to a 27% reduction in PM2.5 (log) concentration. Column (2) displays the regression results with city and year fixed effects included. In Column (3), we further incorporate city-level economic and social characteristics, while Column (4) shows the final regression results with additional weather factors included. We find that the procurement of digital environmental monitoring technology consistently and significantly reduces urban PM2.5 concentrations. The regression results in Column (4) reveal that, holding other factors constant, an additional environmental monitoring contract per 100,000 people is associated with an 8% reduction in PM2.5 (log) concentration, highlighting the importance of including control variables. This result reflects the effectiveness of digital monitoring technology in air pollution management, improving air quality through “monitoring” and “forecasting.” First, digital monitoring technology can significantly enhance the accuracy of pollutant source identification within the detection scope. These data enable governments and regulatory agencies to quickly detect pollution sources and take corresponding remedial actions. Second, digital monitoring, combined with machine learning, can analyze and forecast pollution, which greatly reduces the occurrence of high emissions and severe pollution events. 12 Table 2. Baseline Estimation ln (PM2.5) ln (PM2.5) ln (PM2.5) ln (PM2.5) (1) (2) (3) (4) Environment Monitoring -0.273*** -0.089*** -0.082*** -0.075** (0.039) (0.023) (0.023) (0.022) Economic and social characteristic variables NO NO YES YES Weather characteristic variables NO NO NO YES Time FE NO YES YES YES City FE NO YES YES YES N 922 922 882 882 adj. R-sq 0.0648 0.587 0.668 0.673 Note: This table presents the baseline regression results of the model. Environment Monitoring is our dependent variable, representing the number of contracts per 100,000 people, while ln(PM2.5) represents the logarithms of the annual average PM2.5 in the city. The regression coefficients are estimated using the OLS model. * p < 0.1, ** p < 0.05, *** p < 0.01; Standard errors in parentheses are clustered at the city level. Source: Authors’ calculations. 4.2 Robustness Analyses We conducted a series of regressions to examine the robustness of our main results. Firstly, we excluded the effects of municipalities (Column 1), as their higher administrative levels differ in resource allocation and environmental governance compared to regular cities, making the results more generalizable. Secondly, pilot environmental policies, such as low-carbon cities or carbon emission trading pilots, could cause biases in pollution levels and the effectiveness of monitoring technology, so we also controlled for this influence (Column 2). The PRC is also a major emitter of sulfur dioxide (SO₂), thus we replaced the dependent variable with city-level SO₂ concentration (Column 3) to examine the impact of digital monitoring technology on other air pollutants. We found that for every additional digital monitoring contract per 100,000 people, the SO₂ level decreases by 15%. This finding suggests that digital monitoring technology exerts a greater influence on SO₂ than on PM2.5. This is likely because environmental monitoring technologies primarily target large-scale, high-emission sources, such as coal power plants and steel mills, which are major contributors to SO₂ emissions. 13 Additionally, since digital monitoring equipment has a certain durability period, previously installed equipment may continue to exert influence. We thus measured urban digital environmental monitoring intensity using the cumulative number of environmental monitoring contracts (Column 4). Since the industrial sector is a significant source of PM2.5 emissions, we also calculated the ratio of contract numbers to the share of industrial output in GDP, providing a measure of digital monitoring intensity within the industrial sector (Column 5). Further, as transportation is also a significant source of pollution, we included traffic monitoring in the analysis of air pollution (Column 6). Our coefficient of interest remains significant and slightly larger, suggesting that, beyond its role in identifying traffic violations and managing congestion, traffic monitoring has a spillover effect in pollution control (Column 7). We also replaced the explanatory variable with urban digital monitoring procurement expenditure to re-measure the intensity of digital monitoring technology implementation. While the coefficient of interest is slightly smaller in magnitude and significance than when measured by contract numbers, the significance of all robustness test results consistently indicates the stable suppressive effect of digital environmental monitoring technology on air pollution. 14 Table 3. Robustness Check Note: This table presents the seven robustness tests mentioned. We used OLS estimation to conduct a series of regressions to examine the robustness of our main results. *p < 0.1, ** p < 0.05, *** p < 0.01; Standard errors in parentheses are clustered at the city level. Source: Authors’ calculations. Excluding municipalities Incorporate Policy Pilot ln(SO2) Cumulative Contracts EnvMonit_SecInd Env_trans_cont ract_100k Digital Monitoring values (1) (2) (3) (4) (5) (6) (7) Environment Monitoring -0.075*** -0.073*** -0.158*** (0.022) (0.021) (0.041) ln(Env_cumulative Contracts) -0.043** (0.018) Sec_contracts -0.021** (0.010) Env_trans_contract_ 100k -0.081** (0.029) Digital Monitoring values -0.007* (0.004) Low-carbon city pilot Carbon emission trading pilot NO YES NO NO NO NO NO Control variables YES YES YES YES YES YES YES Time FE YES YES YES YES YES YES YES City FE YES YES YES YES YES YES YES N 873 882 882 1517 882 1089 882 adj. R-sq 0.673 0.674 0.780 0.408 0.676 0.592 0.530 15 4.3 Endogeneity Analysis We employ a two-stage regression with instrumental variables to address potential endogeneity issues in this study. Drawing on the approach by Atkin (2013), we construct a supply shock: ,2014 ,2014 ,2014 i i city i city iI Contract Contract ϕ ∈ =∑ (2) , , ,2014 ln( ) it i t i city iI IV Contract ϕ ∈ = ×∑ (3) Here, represents the proportion of digital environmental monitoring contracts in a given city to the total digital environmental monitoring contracts in the province at the initial sample period, which measures the region’s responsiveness to market opportunities in digital monitoring development. Therefore, the interaction between and the total number of contracts within the province at each time period reflects market-driven shocks in digital monitoring development. As shown in Table 4, the F-statistics are greater than 10. The result indicates that, after controlling for market shocks, the effect of digital monitoring technology on PM2.5 suppression is more pronounced (with a coefficient of -0.28). This partly suggests that market responsiveness and external shocks play a more significant role in the promotion of technology than simple policydriven efforts. 2014,i ϕ 2014,i ϕ 16 Table 4. Results for Endogeneity Test IV estimation (1) Second-stage regression Environment Monitoring -0.28 * (0 .160) Economic and social characteristic variables YES Weather characteristic variables YES Time FE YES City FE YES First-stage regression Industry shock_IV 0.143 *** (0.013) Control variables YES Time FE YES City FE YES F-stat. 114.35 N 877 Note: This table presents the regression results estimated using provincial public security monitoring contracts as the IV variable. The model includes a total of 1,021 samples, and the regression coefficients were obtained using 2SLS estimation. In all models, control variables, year fixed effects, and city fixed effects are fully controlled. *p < 0.1, ** p < 0.05, *** p < 0.01; Standard errors in parentheses are clustered at the city level. Source: Authors’ calculations. 4.4 Mechanism Analysis Digital technology serves primarily as a tool for data analysis and decision support, rather than directly reducing pollutant concentrations. Its impact is most evident in its indirect role, where it enhances governance efficiency by facilitating information flow and strengthening decisionmaking. As shown in Column (1), environmental monitoring technology significantly improves the identification of pollution cases; the coefficient indicates that each additional environmental monitoring procurement contract per 100,000 people is associated with a 46% increase in environment administrative penalty cases identified by the government. This finding indicates that procuring digital monitoring technology significantly enhances transparency in both information disclosure and regulatory enforcement. By reducing information asymmetry in the principal-agent relationship between polluting enterprises and the government, digital monitoring strengthens accountability (Liu, Uchida, and Bao 2024). The increased risk of regulatory scrutiny directly compels enterprises to adopt more advanced environmentally friendly technologies in their production and management processes. To further investigate this effect, we examined the impact of digital environmental technology on 17 different types of enterprises, finding that heavily polluting firms are most affected. As indicated in Column (2), each additional environmental monitoring procurement contract per 100,000 people is associated with a 73.3% reduction in the number of heavily polluting enterprises in a city. Tiantian et al. (2024) observed similar trends, noting that increased regulatory intensity has driven the systematic elimination of outdated production capacities in the industrial sector, including government-led closures of small, heavily polluting factories. This outcome is largely because of the substantial compliance costs associated with adopting advanced clean technologies, which pose particular challenges for pollution-intensive industries (Zhang and Li 2023). On the other hand, the Porter Hypothesis posits that stricter environmental regulations ultimately enhance corporate green performance and competitiveness (Zhang et al. 2024, Cohen and Tubb 2017). To explore this, we examined the impact of adopting digital environmental monitoring technology on the green patents co-invented by enterprises in the city. The regression results in Column (3) indicate that digital monitoring technology accelerates waste utilization and reduces energy consumption, leading to “innovation compensation.” Table 5. Results for Mechanism Analysis Administrative penalty(log) Polluting Firm(log) Joint Green Patents(log) (1) (2) (3) Environment Monitoring 0.463** -0.733* 0.378** (0.226) (0.374) (0.152) Economic and social characteristic variables YES YES YES Weather characteristic variables YES YES YES Time FE YES YES YES City FE YES YES YES N 715 773 704 adj. R-sq 0.041 0.361 0.110 Note: The table records three possible mechanisms through which digital environmental monitoring technology affects urban PM2.5 levels. Column (1) represents the impact of environmental monitoring technology on the number of urban environmental protection cases; Column (2) shows the impact of environmental monitoring technology on the number of heavily polluting enterprises in the city; Column (3) represents the impact of environmental monitoring technology on the number of green patents in the city. In all models, control variables, year fixed effects, and city fixed effects are fully controlled. *p < 0.1, ** p < 0.05, *** p < 0.01; Standard errors in parentheses are clustered at the city level. Source: Authors’ calculations. 18 4.5 Heterogeneity Analysis 4.5.1 Public Interest and Government Attention As previously outlined, digital environmental monitoring technology functions primarily as an “information provider,” while the government, enterprises, and individuals act as the actual “users” of this data. Increased attention to air quality data often reflects higher expectations for the environment, and the supervisory pressure resulting from this heightened awareness is more likely to translate into actual pollution control decisions, driving environmental improvement. Our analysis focuses on the effects of public and governmental attention on environmental pollution. Public interest is measured using the Baidu Search Index, whereas government attention is assessed through the frequency of environmentally related terms in government work reports—a widely adopted approach in environmental regulation studies (Tu et al. 2024, Chen and Chen 2018). Based on median values, both attention metrics are categorized into high and low groups. The regression results are presented in Table 6. In regions with higher public environmental concern, an additional procurement contract per 100,000 people leads to a 10% reduction in PM2.5 levels (Column 1). This result underscores the importance of public influence in environmental governance. As public concern for the environment increases, governments are likely to face greater regulatory pressure, leading to stricter pollution control measures (Wang and Jia 2021, Wang and Cao 2024). Similarly, in regions with higher levels of government environmental concern, each additional environmental monitoring procurement contract per 100,000 residents corresponds to a 9% reduction in PM2.5 levels (Column 3). Comparing these coefficients shows that public attention exerts a stronger environmental impact than government concern. This can be attributed to sustained public attention, which typically fosters stronger social oversight and environmental feedback, thereby enhancing pollution control effectiveness. However, in areas with lower levels of public and government environmental concern, we do not observe a significant impact of digital environmental monitoring on pollution control. This result highlights the synergy between 19 technology and human behavior (i.e., social attention and government decision-making), highlighting the conditions and dependencies for technology to achieve effective outcomes. Table 6. Impact on Public Interest and Government Attention High_public interest Low_public interest High_government attention Low_government attention (1) (2) (3) (4) Environment Monitoring -0.103** -0.038 -0.088** -0.051 (0.033) (0.031) (0.027) (0.030) Economic and social characteristic variables YES YES YES YES Weather characteristic variables YES YES YES YES Time FE YES YES YES YES City FE YES YES YES YES P-value of inter-group difference coefficient 0.050** 0.049** N 606 276 483 399 adj. R-sq 0.742 0.655 0.770 0.661 Note: This table shows the heterogeneity of the impact of digital environmental monitoring technology on PM2.5 under public and government environmental concern, with the median used as the grouping standard. Columns (1)–(2) represent the heterogeneity of public environmental interest on urban PM2.5, while Columns (3)–(4) represent the heterogeneity of government environmental concern. We used a two-sided t-test to examine the significance of differences between coefficients across different groups. In all models, control variables, year fixed effects, and city fixed effects are fully controlled. * p < 0.1, ** p < 0.05, *** p < 0.01; Standard errors in parentheses are clustered at the city level. Source: Authors’ calculations. 4.5.2 Environmental Information Disclosure Public interest in environmental information is predicated on the accessibility and transparency of environmental data. We use the Pollution Information Transparency Index (PITI) to measure the level of environmental information disclosure in PRC cities, with core evaluation content including the disclosure of enterprise emissions information, environmental regulatory transparency, air quality information, and public participation. Because of data availability, we obtained the environmental information disclosure index for 120 cities from 2014 to 2018. The regression results are shown in Table 7. In regions with higher levels of environmental information disclosure, each additional environmental monitoring procurement contract per 20 100,000 residents corresponds to a 7% reduction in PM2.5 levels (Column (1)). This finding underscores the critical role of information disclosure in effective environmental governance. A lack of transparent environmental data can encourage lax production practices among enterprises and foster rent-seeking behavior among government officials. Enhanced transparency, however, directly restricts opportunities for officials to benefit from concealing information, thereby reinforcing public oversight and bolstering government accountability (Wei and He 2022, Chen et al. 2022). We further present our baseline regression coefficients, heterogeneity by attention level, and information disclosure through coefficient visualization (Fig. 4). Table 7. Impact on Information Disclosure ln(PM2.5) (1) (2) High information disclosure Low information disclosure Environment Monitoring -0.069** -0.011 (0.028) (0.036) Economic and social characteristic variables YES YES Weather characteristic variables YES YES Time FE YES YES City FE YES YES P-value of inter-group difference coefficient 0.093* N 403 340 adj. R-sq 0.677 0.761 Note: This table shows the impact of digital environmental monitoring on PM2.5 under the heterogeneity of information disclosure. 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The findings show that following the adoption of digital environmental monitoring technology: (i) city-level PM2.5 concentrations exhibit a sizeable reduction, (ii) regulatory enforcement improves and heavily polluting firms exit more, (iii) green innovation is enhanced through more accurate pollutant identification, and (iv) the effectiveness of these technologies depends on the extent of information disclosure and public participation. About the Asian Development Bank ADB is a leading multilateral development bank supporting inclusive, resilient, and sustainable growth across Asia and the Pacific. Working with its members and partners to solve complex challenges together, ADB harnesses innovative financial tools and strategic partnerships to transform lives, build quality infrastructure, and safeguard our planet. Founded in 1966, ADB is owned by 69 members—50 from the region. DIGITAL MONITORING TECHNOLOGY AND AIR QUALITY EVIDENCE FROM THE PEOPLE’S REPUBLIC OF CHINA Pinghan Liang, Yadi Liu, and Shu Tian