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The objective of this paper is to explore how environmental protection expenditures (EPE) affect innovation and investigate the potential causal relationship between the two variables. The novelty and main contribution of our paper to the literature stems from the fact that we look at total environmental protection expenditures from both the public and private sectors, and distinguishing between corporations and households in the private sectors. We also consider both inputs and outputs of innovation and address potential endogeneity issues in this relationship, which are both lacking in the current literature. Using a panel of EU countries over the period 2006–2020, this study generally finds a positive impact of EPE on innovation after controlling for the effects of trade, credit, education and inflation on innovation, and using the System GMM estimator to treat endogeneity. Moreover, EPE from the government sector drives the bulk of this positive impact. The core results are robust to alternative measures of innovation, alternative estimation techniques that address endogeneity (i.e., the Lewbel 2SLS, Mundlak, and Hausman-Taylor estimators) and the moderation effects of Climate Change Performance (CCPI). This study also provides a useful guide to policy makers engaged in environmental planning. • We evaluate the impact of environmental protection expenditures (EPE) on innovation in mainly EU countries. • We use longitudinal dataset for the group of EU countries over the period 2006–2020. • We find a positive impact of EPE on innovation. • The results are robust for observable heterogeneity, alternative measures of innovation and estimation techniques. • This study provides a useful guide to policy makers engaged in environmental planning. [ABSTRACT FROM AUTHOR]

Energy conservation is a fundamental requirement in achieving sustainable development, enhancing environmental protection and improving economic and social wellbeing by reducing energy consumption. Carbon emissions are a significant manifestation of energy consumption. Real-time monitoring and forecasting of air quality plays a crucial role in effectively controlling carbon emissions and improving energy conservation measures. Complex Event Processing (CEP) is a technical framework for streaming data processing based on predefined rules and is widely used for real-time detection. Yet as the diversity of data increases, it becomes exceptionally difficult for experts to formulate the CEP rules. Rule-based algorithms can address this issue to a certain extent. However, the CEP rules extracted through these methods are obtained from static data and are often simple and independent of each other. Here, we regard such rules as direct rules, which ignore possible connections between events that usually imply deeper rules. In contrast, we refer to rules that reflect interconnections between events and reveal higher-level principles as indirect rules. In this paper, we propose a methodology fusing interpretable machine learning and decision mining to extract direct and indirect multi-level CEP rules from air pollution datasets for real-time detection, prediction, and early warning. First, we utilize the decision tree algorithm to extract direct rules to detect air quality in real-time. Next, we utilize the process mining algorithm to model the changes in air states and extract the Petri net model from it that reflects the changes in air quality. Then, we utilize our proposed decision mining algorithm to extract indirect CEP rules. Indirect rules can help to understand how pollutants in the air change over time. In addition, they can reveal underlying trends and patterns of air quality change for prediction and early warning. Finally, we validate the accuracy and effectiveness of our approach using a real air pollution dataset. This work contributes to the field of air pollution detection and can help promote the optimization of energy conservation, while it can also support the development of regulatory strategies. • A multi-level CEP rules extraction method is proposed for air quality detection. • A novel method combining machine learning and process mining to extract CEP rules. • A novel way to analyze decision points in Petri nets to monitor air states changes. [ABSTRACT FROM AUTHOR]

After-treatment devices of diesel engines effectively reduce exhaust emissions but deteriorate over time. Consequently, the China-VI emission regulation mandates a useful life of seven years or 700,000 km for after-treatment devices. Conventional engine bench aging methods, being costly and time-consuming, are inapplicable for long-term emission durability verification. This paper introduces an accelerated aging method based on the thermal accumulation and Arrhenius equation. Accelerated aging of four after-treatment devices of the same model at different temperatures and durations was carried out. Results revealed that barring the CDPF's PN filtration efficiency, the efficiencies of the DOC, SCR, and ASC catalysts declined with accelerated aging, especially under lower temperatures. Deterioration in conversion efficiencies of catalysts increased the NOx, CO, THC, and NH 3 emissions under cold- and warm-WHTC tests. Considering that the equivalent durability mileage of the after-treatment devices under 150 h 650 °C accelerated aging condition is approximately 870,000 km, while the CO, THC, PN, NOx, and NH 3 emission factors are 0.05 g/kWh, 0.21 g/kWh, 2.14 × 1010 #/kWh, 0.44 g/kWh, and 3.48 ppm, respectively, so the durability of the tested after-treatment devices could comply with the China-VI emission regulation. Although accelerated aging did not significantly affect PN emissions, it increased the balanced point temperature of CDPF and the proportion of sub-23 nm particles. If emission regulations further reduce the lower limit of PN measurement to 10 nm, it could negatively impact the emission durability of CDPF. In addition, comparing the 1000 h of conventional aging showed similar characteristics and results in NOx conversion efficiency and NOx emission, suggesting that the accelerated aging method is reliable. In conclusion, the accelerated aging method reduces the durability verification duration, providing a cost-effective regulatory tool for environmental protection authorities and guidance for manufacturers to optimize device performance and durability. • Accelerated aging method for after-treatment devices of diesel engines is developed • Catalyst performance deteriorates more seriously under low temperatures • NOx deterioration is most pronounced during accelerated aging • Accelerated aging is well-equated with conventional bench aging based on SCR [ABSTRACT FROM AUTHOR]