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This book reviews quantitative methods and models of impact evaluation. The formal literature on impact evaluation methods and practices is large, with a few useful overviews. Yet there is a need to put the theory into practice in a hands-on fashion for practitioners. This book also details challenges and goals in other realms of evaluation, including monitoring and evaluation (M&E), operational evaluation, and mixed-methods approaches combining quantitative and qualitative analyses. This book is organized as follows. Chapter two reviews the basic issues pertaining to an evaluation of an intervention to reach certain targets and goals. It distinguishes impact evaluation from related concepts such as M&E, operational evaluation, qualitative versus quantitative evaluation, and ex-ante versus ex post impact evaluation. Chapter three focuses on the experimental design of an impact evaluation, discussing its strengths and shortcomings. Various non-experimental methods exist as well, each of which are discussed in turn through chapters four to seven. Chapter four examines matching methods, including the propensity score matching technique. Chapter five deal with double-difference methods in the context of panel data, which relax some of the assumptions on the potential sources of selection bias. Chapter six reviews the instrumental variable method, which further relaxes assumptions on self-selection. Chapter seven examines regression discontinuity and pipeline methods, which exploit the design of the program itself as potential sources of identification of program impacts. Specifically, chapter eight presents a discussion of how distributional impacts of programs can be measured, including new techniques related to quantile regression. Chapter nine discusses structural approaches to program evaluation, including economic models that can lay the groundwork for estimating direct and indirect effects of a program. Finally, chapter ten discusses the strengths and weaknesses of experimental and non-experimental methods and also highlights the usefulness of impact evaluation tools in policy making.

Human activity has significantly increased the deposition of nitrogen (N) on terrestrial ecosystems over pre‐industrial levels leading to a multitude of effects including losses of biodiversity, changes in ecosystem functioning, and impacts on human well‐being. It is challenging to explicitly link the level of deposition on an ecosystem to the cascade of ecological effects triggered and ecosystem services affected, because of the multitude of possible pathways in the N cascade. To address this challenge, we report on the activities of an expert workshop to synthesize information on N‐induced terrestrial eutrophication from the published literature and to link critical load exceedances with human beneficiaries by using the STressor–Ecological Production function–final ecosystem Services Framework and the Final Ecosystem Goods and Services Classification System (FEGS‐CS). We found 21 N critical loads were triggered by N deposition (ranging from 2 to 39 kg N·ha−1·yr−1), which cascaded to distinct beneficiary types through 582 individual pathways in the five ecoregions examined (Eastern Temperate Forests, Marine West Coast Forests, Northwestern Forested Mountains, North American Deserts, Mediterranean California). These exceedances ultimately affected 66 FEGS across a range of final ecosystem service categories (21 categories, e.g., changes in timber production, fire regimes, and native plant and animal communities) and 198 regional human beneficiaries of different types. Several different biological indicators were triggered in different ecosystems, including grasses and/or forbs (33% of all pathways), mycorrhizal communities (22%), tree species (21%), and lichen biodiversity (11%). Ecoregions with higher deposition rates for longer periods tended to have more numerous and varied ecological impacts (e.g., Eastern Temperate Forests, eight biological indicators) as opposed to other ecoregions (e.g., North American Deserts and Marine West Coast Forests each with one biological indicator). Nonetheless, although ecoregions differed by ecological effects from terrestrial eutrophication, the number of FEGS and beneficiaries impacted was similar across ecoregions. We found that terrestrial eutrophication affected all ecosystems examined, demonstrating the widespread nature of terrestrial eutrophication nationally. These results highlight which people and ecosystems are most affected according to present knowledge, and identify key uncertainties and knowledge gaps to be filled by future research.

During the building design phase, a lot of attention is paid to the thermal properties of the external envelopes. New regulations are introduced to improve energy efficiency of a building and impose a reduction of the overall heat transfer coefficient; meanwhile, this efficiency is more influenced by the efficiency of the heating system and the type of fuels used. This article presents a complex analysis including the impact of: heat transfer coefficient of the envelope, efficiency of building service systems, the type of energy source, and the fuel. The analysis was based on the results of simulation tests obtained for an exemplary multi-family residential building located in Poland that is not equipped with a cooling system. The conducted calculations gave quantitative evaluation of the influence of particular parameters on building energy performance and showed that the decrease of heat transfer coefficient of building boundaries, in accordance to the Polish regulation for 2017 and 2021, gave only 11% of reduction on usable energy demand index. On the other hand, it was found that modification of the heating system and heat source can significantly influence the values of the final and primary energy consumption at the level of 70%. The application of heat pumps has a greater influence on the final and primary energy consumption for heating indices than other parameters, such as the building’s envelopes.

Reducing the energy intensity of economies is key to meeting sustainable development goals. In the past, energy intensity has generally decreased or has shown inverted U-shape patterns. However, most global energy scenarios project unrealistically increasing relative decoupling rates of primary and final energy when compared to the observed historical trends. Here, we develop a final energy intensity decomposition which considers both productive and non-productive sectors, includes both traditional and commercial sources, and uses the exergy metric to aggregate different energy flows. We study the Portuguese economy between 1960 and 2014, a period of major energy and economic transitions. First, we find that the strong decrease of final energy intensity during the period of highest economic growth (1960–1974) is mainly driven by the following: (1) the efficiency increase in the residential sector, due to the transition from traditional sources (firewood) to electricity; (2) the relatively slow growth of energy use in the residential sector; and (3) the efficiency improvements in the productive sectors. We find that the second factor was determined by the fact that increasing per capita economic consumption was not channeled through private energy use. Second, excluding the last decade, private transportation had a growing effect on final energy intensity throughout the whole time range. Overall, we find that, essentially, remarkable technical improvements in terms of increasing final-to-useful energy efficiency made possible a relative decoupling at the final stage.

In this study, a structural decomposition method was applied to research the factors affecting the changes in air pollution emissions in China. Based on 1995–2009 data from the World IO Database, we combine China’s (Import) Noncompetitive IO Table and the Environmental Account Table. The results indicate that emission intensities represent the most important factor for reducing air pollution emissions in China. In contrast, economies of scale and the intermediate input product structure constitute the major causes for acceleration in the growth of air pollution emissions in China. From the perspective of final demand, the economic scale effect caused by investment demand is the main reason for this accelerated growth in China’s air pollution emissions in recent years. Consumption-driven economic growth is cleaner, while investment-driven economic growth is dirtier. This study constructed a structure decomposition model based on the input-output tables, which is suitable for studying the driving forces of various economic indicators, such as energy, carbon dioxide, and economic growth. At the same time, this method is helpful for analyzing the factors that influence changes in economic indicators that result from different economic pull modes, such as the final demand mode. However, the model does have limitations; for example, it does not consider the difference between general trade and processing trade in exports.

Government budget management serves as a critical enabler for the development of a green economy and represents an essential pathway to promote sustainable urban development. The government budget delineates the scope and direction of governmental activities, while the advancement of a green economy heavily relies on the support of budgetary funds. Adopting the perspective of government budget management capabilities, this study examines the budget deviations across 288 prefecture-level cities in China from 2007 to 2021. By constructing a double fixed-effects model, we assess whether government budget management can effectively balance environmental governance with sustainable economic development, thereby fostering green economic growth. The findings indicate that government budget management indeed achieves this balance, with revenue management playing a more significant role compared to expenditure management. Mechanism analyses reveal that at the revenue level, government budget management regulates local economic behavior through tax constraints, while at the expenditure level, it drives green economic development by promoting technological innovation. Heterogeneity analysis further demonstrates that geographical differences, humanistic environment factors, and the degree of marketization significantly influence the development of the green economy. Based on these insights, this paper proposes targeted policy recommendations aimed at mitigating the tension between environmental governance and sustainable economic development and facilitating the attainment of green economic objectives ultimately.

Background Accelerated intermittent theta burst stimulation (aiTBS), which involves the administration of multiple daily sessions of iTBS, represents a novel regimen of repetitive transcranial magnetic stimulation. Studies have suggested that aiTBS may facilitate a fast response among patients with major depressive disorders. However, whether aiTBS can accelerate antidepressant response in adolescents suffering from depression is still unclear. Additionally, the potential indicators associated with antidepressant response in this population are still understudied. Methods Ninety adolescents with depression were recruited and randomly assigned to aiTBS (two 600-pulse sessions of iTBS spaced for 10 min, N  = 31), iTBS (one 600-pulse session, N  = 29), or sham iTBS ( N  = 30) for two treatment weeks. Kaplan–Meier analysis was used to estimate the mean time to antidepressant response among the three groups. The analysis of covariance and the multiple logistic regression were applied to identify potential indicators associated with treatment response. Results The mean time to antidepressant response was 7.45 weeks (95% CI: 6.19–8.72) in the aiTBS group, 5.62 weeks (95% CI: 4.09–7.16) in the iTBS group, and 5.07 weeks (95% CI: 3.56–6.58) in the sham group, respectively. The log rank test revealed no significant difference in the mean time to antidepressant response among the three groups (χ 2  = 4.156, p  = 0.125). For the antidepressant response, there were also no significant interactions between iTBS treatment regimens and the baseline characteristics. Notably, participants with higher motor threshold and worse global function at baseline were likely to be associated with early response and final response, respectively, while those who experiencing child-parent separation were associated with both early and final response. In addition, younger participants were more likely to experience recurrence during follow-up. Conclusions aiTBS did not demonstrate an advantage in terms of a fast antidepressant response. However, some pretreatment characteristics might serve as indicators of antidepressant response. This relatively simple application based on pretreatment characteristics seems to be a cost-effective method to identify adolescents who are more likely to develop an early antidepressant response and sustain it. Trial registration This is a secondary analysis of a primary RCT, which was officially registered in the Chinese Clinical Trial Registry at 19/1/2021 with the number of ChiCTR2100042346. https://www.chictr.org.cn/bin/project/edit?pid=66118 .

PurposeThe purpose of the study is to evaluate the relationship of Journal Publication Timeline (submission to first decision and submission to final decision) with various Journal Metrics (citing half-life, article influence score, the immediacy index, the acceptance rate, the impact factor (IF), five years IF, Eigenfactor and cited half-life) of top 600 journals retrieved from Journal Citation Report (JCR) 2020 under the tag, Elsevier Unified.Design/methodology/approachTop 600 journals in the decreasing order of the IFs under the tag, “Elsevier Unified” were retrieved from JCR 2020 of Clarivate Analytics. Information about “Journal Metrics” was ascertained using “Customized Service” of JCR, while information about the “Publication Timeline” of each journal was obtained using Elsevier's “Journal Insights Service.” It was found that only 177 journals provided the complete information regarding the “Publication Timeline” and hence considered for the study. Descriptive statistics and correlation analysis was conducted to test the different hypotheses.FindingsIt was found that submission to first decision has a significant relationship with the immediacy index, citing half-life and the acceptance rate. Submission to final decision has a significant relationship with Journal Impact Factor (JIF), the immediacy index, Eigenfactor, citing half-life and the acceptance rate.Research limitations/implicationsThe study will provide the authors with sound and valuable information to support their selection of journals. Inferences in light of fluctuations in the scholarly communication process in terms of Publication Timelines and Journal Metrics can be deeply understood with the aid of the current study's findings. What considerations authors have to take before submitting their papers is the main implication of the study. Journal administrators can also benefit from the findings of the current study as it can help recruit and manage reviewers, which will ensure a successful publication timeline.Originality/valueThe study correlates Publication Timeline Indicators with Journal Metrics Indicators using secondary cross-sectional data. Though most previous studies only examine the relationship of the Publication Timeline with the Journal Impact Factor (JIF), there is very scarce literature that deciphers the influence of Publication Timeline indicators on different Journal Metrics indicators (including JIF).Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2022-0108.

Establishing an effective early warning mechanism for the rocket final assembly process (RFAP) is crucial for the timely delivery of rockets and the reduction of additional production costs. To solve the unsystematic design of warning indicators and warning levels in RFAP and address the problem of low warning accuracy caused by imbalanced data distribution, this paper redesigns the warning indicators and warning levels in a systematic way, and develops a novel multiclass imbalanced learning method based on dynamic sampling algorithm (DyS) and improved ensemble neural network (IENN). The DyS algorithm dynamically determines the training set after oversampling the minority class, while the IENN can effectively suppress the oscillation in the iterative process of the DyS algorithm and improve the overall classification accuracy by removing the redundant and ineffective networks from the ensemble neural network. The experiment results indicate that the proposed method outperforms other methods in terms of accuracy and stability for early warning of tardiness in RFAP.

Countries participating in the Amazon Cooperation Treaty Organization have few options for the environmentally appropriate final disposal of municipal solid waste. Thus, sustainable practices aimed at reducing the negative effects of such a disposal on the environment are complex and hard to accomplish, since solid waste generation per capita proportionally increases as populations grow (≈ 2.7% > world average), mainly in countries inserted in Amazon Cooperation Treaty Organization. Thus, demographic, socioeconomic, management, and ecological factors represented by 18 independent variables were statistically analyzed to explain waste per capita variation in Amazonian countries and sub-regions. Multiple Kruskal–Wallis tests were applied; 13 of them recorded significant results ( p  < 0.05). Subsequently, simple and multivariate regression analyses were carried out by taking into consideration waste per capita and significant variables. Simple regression results recorded for variables “IAC” and “Gini index” were significant ( R IAC 2  = 60.09%, R Gini 2  = 30.83%), with emphasis on “Amazon biome” (DF = 33, p  < 0.01, R Biome 2  = 5.34%). Multivariate models resulted in wide explainability variation, depending on the number and type of available variable (54.47% ≤  R aj 2  ≤ 70.83%), with emphasis on “IAC,” “Ptot,” “Purb,” “Wton,” “Lon,” Area, “HDI,” “Gini,” and “SDG11” ( p  < 0.01). In conclusion , waste per capita estimation models can present variations and geographical interdependencies due to different variables and factors that reflect the current public policies and municipal solid waste management practices.