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Transportation electrification aims to mitigate climate change but will also introduce challenges to adaptation planning and management of infrastructure and facilities. Concurrently, these challenges can be amplified by vulnerabilities arising from the growing intense climate and weather events, such as heavy precipitation and tropical cyclones. Given their role in channeling the mobility of residents under environmental shocks, public electric vehicle charging stations (EVCS) need to exhibit resilience, i.e., the ability to withstand, respond to, and recover from disruptions. EVCS service flow can be disturbed not only by physically damaging charging stations but also by impeding the station-user interactions along spatial networks. To our best knowledge, the user-centric resilience of EVCS networks, when confronted with present and anticipated shocks, has not been well studied. We introduce a novel bipartite network of EVCSs and users (BNEU) to conceptualize the resilience of neighborhood charging service flows under environmental shocks using three-level resilience metrics. We then correlate the resilience of BNEU with the physical, socioeconomic, and topological characteristics. Furthermore, we develop a counterfactual analytical framework using a multiagent-based model to simulate users' charging activities in worse-case scenarios of a real-world hurricane, with intensified wind and flood hazards. Through a case study of Hurricane Ian's disturbances on the BNEU in the Tampa Bay area, we find that topologically dispersed subnetworks and stations or user nodes with higher average degree show greater resilience. We also uncover uneven charging opportunities for older adults and low-income populations under disturbances, which could be exacerbated by worse-case hurricane scenarios. The counterfactual analytical framework further informs strategic infrastructure planning for the forward-looking resilience of EVCS network in coastal communities, thus closing the resilience gap in the adaptation of mitigation measures. [ABSTRACT FROM AUTHOR]

• Complete descriptive panel models use absolute numbers on industry growth. • Decomposes agglomeration effects into industry, structure and spillover effects. • Identifies inter-industry relationships that define evolutionary growth regimes. • Simulation scenarios with manipulated values or coefficients reflect interventions. • The application finds compensatory growth that confirms a previous lock-in. Inter-industry relationships constitute growth regimes in regional evolutionary developments. The paper proposes a regression-based counterfactual simulation approach with location-level industry data in order to analyse systematically how the relationships between different industries and the growth regimes these relationships constitute differ across regions, and how these differences affect the response of regional development to interventions. At the core of the approach are complete descriptive panel regression models that decompose agglomeration effects into industry, spillover, and structure effects. With the identified coefficients simulations are carried out. To demonstrate its advantages, we apply the approach to the analysis of path-dependent employment growth in a livestock-intensive German location facing capacity constraints. In some of the scenarios, where individual industries are affected by exogenous shocks, we observe compensatory growth in other industries. This confirms the occurrence of evolutionary dynamics and the relevance of approaches that recognise and reproduce them. [ABSTRACT FROM AUTHOR]

Despite the negative externalities on the environment and human health, today's economies still produce excessive carbon dioxide emissions. As a result, governments are trying to shift production and consumption to more sustainable models that reduce the environmental impact of carbon dioxide emissions. The European Union, in particular, has implemented an innovative policy to reduce carbon dioxide emissions by creating a market for emission rights, the emissions trading system. The objective of this paper is to perform a counterfactual analysis to measure the impact of the emissions trading system on the reduction of carbon dioxide emissions. For this purpose, a recently-developed statistical machine learning method called matrix completion with fixed effects estimation is used and compared to traditional econometric techniques. We apply matrix completion with fixed effects estimation to the prediction of missing counterfactual entries of a carbon dioxide emissions matrix whose elements (indexed row-wise by country and column-wise by year) represent emissions without the emissions trading system for country-year pairs. The results obtained, confirmed by robust diagnostic tests, show a significant effect of the emissions trading system on the reduction of carbon dioxide emissions: the majority of European Union countries included in our analysis reduced their total carbon dioxide emissions (associated with selected industries) by about 15.4% during the emissions trading system treatment period 2005–2020, compared to the total carbon dioxide emissions (associated with the same industries) that would have been achieved in the absence of the emissions trading system policy. Finally, several managerial/practical implications of the study are discussed, together with its possible extensions. [ABSTRACT FROM AUTHOR]

Purpose/Goal: This research investigates the potential link between mobility and violent crimes in Tamil Nadu, India, using an empirical study centred on the COVID-19 pandemic waves (2020–2022). The goal is to understand how these events influenced crime, employing a counterfactual approach. Methods: The study employs the XGBoost algorithm to forecast counterfactual events across different timeframes with varying levels of mobility. The mobility data sources include historical bus and passenger records spanning a decade, along with Google Community Mobility Reports added during the pandemic phases. The foundation for crime analysis is built upon the univariate time series of violent crimes reported as First Information Reports from 2010 to 2022. Findings: Results indicate a significant correlation between mobility and violent crimes when mobility drops below a specific threshold. However, no such correlation is observed when mobility is above this threshold during the non-pandemic periods. The COVID-19 pandemic had a major impact on people's and vehicular mobility, especially during the complete lockdown periods of the first two waves, and also affected crime rates. Conclusions: The decrease in recorded incidents could also be attributed to fewer criminal opportunities. Additionally, this could be due to unfavourable situational factors, such as victims' limited access to appropriate health and law enforcement agencies to report crimes. Furthermore, frontline services were busy with pandemic-related commitments, which could have contributed to a lack of crime registration even when crimes were committed. [ABSTRACT FROM AUTHOR]

This study combined the World Input–Output Database and Asian Development Bank's Multiregional Input–Output database to investigate Canada's embodied CO2 emissions in exports (EEE) for the period of 2000–2018. We examined the key drivers and paths through structural decomposition analysis and structural path analysis. First, the results showed that embodied emissions in the intermediate exports were the major contributor to Canada's EEE, and emission paths involving more than three countries were on the rise, indicating that the expansion of the global industrial supply chains has complicated the paths of Canada's EEE. Second, the factors such as emission intensity of sectors, export structure, and export scale, had varying influences on Canada's EEE over time. For several sectors, the benefit from reduced emission intensity was largely offset by the additional emissions from the increased export scale. Hence, the design of emission regulations should consider the heterogeneity of industrial sectors in order to mitigate emissions for the diverse industries in Canada. Third, energy and resource industries (e.g., electricity, petroleum, wood, metals, and so on) played an essential role in Canada's exports. A significant amount of embodied emissions was transferred from these sectors to the downstream sectors along the supply chain, indicating that abatement measures should be adopted from the whole life cycle perspective of a product/service through an integrated governance of the supply chain. This article met the requirements for a Gold–Silver JIE data openness badge described at http://jie.click/badges. [ABSTRACT FROM AUTHOR]

Existing research has shown that political crisis events can directly impact the tourism industry. However, the current methods suffer from potential changes of unobserved variables, which poses challenges for a reliable evaluation of the political crisis impacts. This paper proposes a panel counterfactual approach with Internet search index, which can quantitatively capture the change of crisis impacts across time and disentangle the effect of the event of interest from the rest. It also provides a tool to examine potential channels through which the crisis may affect tourist outflows. This research empirically applies the framework to analyze the THAAD event on tourist flows from the Chinese Mainland to South Korea. Findings highlight the strong and negative short-term impact of the political crisis on the tourists' intentions to visit a place. This paper provides essential evidence to help decision-makers improve the management of the tourism crisis. [ABSTRACT FROM AUTHOR]

Visitor attraction dynamics lead tourism industry paths. A complex artificial neural network model was built to predict the incoming tourism flow in the Apulia region of Southern Italy as a function of the heterogeneity of the tourism supply available in this area. Open data from the Regional Tourism Observatory were targeted. Information on the distribution of facilities and activities that attract regional tourist flows was collected and grouped by municipality. An artificial neural network model was built with total tourist attendance as the dependent variable and tourist attractions as regressors. The Root Mean Square Error (RMSE) was used to select the optimal model using the lowest value. The final model was run with a hidden layer consisting of three neurons and a decay value of 0.01. A Multi-Objective Counterfactual model (MOC) was then constructed using a randomly selected row of normalized data frame to validate a useful tool in increasing total tourist attendance by 20% over that of the randomly selected municipality. A Garson's variables importance plot indicated natural landscapes such as beaches, sea caves, and natural parks have a primary role expressed in terms of variable importance in the AI algorithm when used as an innovative methodology for evaluating tourism flows in the Apulia region. A further MOC model built using a randomly selected row of normalized data frame showed convents, libraries, historical buildings, public gardens, and museums as the top five features most modified to improve total attendance in a randomly selected municipality. Use of AI modeling revealed that the implementation of nature-based solutions may speed up the flow of tourism in the Apulia region while also promoting sustainable social development. [ABSTRACT FROM AUTHOR]

Abstract Despite the negative externalities on the environment and human health, today’s economies still produce excessive carbon dioxide emissions. As a result, governments are trying to shift production and consumption to more sustainable models that reduce the environmental impact of carbon dioxide emissions. The European Union, in particular, has implemented an innovative policy to reduce carbon dioxide emissions by creating a market for emission rights, the emissions trading system. The objective of this paper is to perform a counterfactual analysis to measure the impact of the emissions trading system on the reduction of carbon dioxide emissions. For this purpose, a recently-developed statistical machine learning method called matrix completion with fixed effects estimation is used and compared to traditional econometric techniques. We apply matrix completion with fixed effects estimation to the prediction of missing counterfactual entries of a carbon dioxide emissions matrix whose elements (indexed row-wise by country and column-wise by year) represent emissions without the emissions trading system for country-year pairs. The results obtained, confirmed by robust diagnostic tests, show a significant effect of the emissions trading system on the reduction of carbon dioxide emissions: the majority of European Union countries included in our analysis reduced their total carbon dioxide emissions (associated with selected industries) by about 15.4% during the emissions trading system treatment period 2005–2020, compared to the total carbon dioxide emissions (associated with the same industries) that would have been achieved in the absence of the emissions trading system policy. Finally, several managerial/practical implications of the study are discussed, together with its possible extensions.

Abstract Purpose/Goal This research investigates the potential link between mobility and violent crimes in Tamil Nadu, India, using an empirical study centred on the COVID-19 pandemic waves (2020–2022). The goal is to understand how these events influenced crime, employing a counterfactual approach. Methods The study employs the XGBoost algorithm to forecast counterfactual events across different timeframes with varying levels of mobility. The mobility data sources include historical bus and passenger records spanning a decade, along with Google Community Mobility Reports added during the pandemic phases. The foundation for crime analysis is built upon the univariate time series of violent crimes reported as First Information Reports from 2010 to 2022. Findings Results indicate a significant correlation between mobility and violent crimes when mobility drops below a specific threshold. However, no such correlation is observed when mobility is above this threshold during the non-pandemic periods. The COVID-19 pandemic had a major impact on people’s and vehicular mobility, especially during the complete lockdown periods of the first two waves, and also affected crime rates. Conclusions The decrease in recorded incidents could also be attributed to fewer criminal opportunities. Additionally, this could be due to unfavourable situational factors, such as victims’ limited access to appropriate health and law enforcement agencies to report crimes. Furthermore, frontline services were busy with pandemic-related commitments, which could have contributed to a lack of crime registration even when crimes were committed.

Biodiversity offsetting is a globally influential policy mechanism for reconciling trade-offs between development and biodiversity loss. However, there is little robust evidence of its effectiveness. We evaluated the outcomes of a jurisdictional offsetting policy (Victoria, Australia). Offsets under Victorias Native Vegetation Framework (2002-2013) aimed to prevent loss and degradation of remnant vegetation, and generate gains in vegetation extent and quality. We categorised offsets into those with near-complete baseline woody vegetation cover (avoided loss, 2702 ha) and with incomplete cover (regeneration, 501 ha), and evaluated impacts on woody vegetation extent from 2008 to 2018. We used two approaches to estimate the counterfactual. First, we used statistical matching on biophysical covariates: a common approach in conservation impact evaluation, but which risks ignoring potentially important psychosocial confounders. Second, we compared changes in offsets with changes in sites that were not offsets for the study duration but were later enrolled as offsets, to partially account for self-selection bias (where landholders enrolling land may have shared characteristics affecting how they manage land). Matching on biophysical covariates, we estimated that regeneration offsets increased woody vegetation extent by 1.9%-3.6%/year more than non-offset sites (138-180 ha from 2008 to 2018) but this effect weakened with the second approach (0.3%-1.9%/year more than non-offset sites; 19-97 ha from 2008 to 2018) and disappeared when a single outlier land parcel was removed. Neither approach detected any impact of avoided loss offsets. We cannot conclusively demonstrate whether the policy goal of net gain (NG) was achieved because of data limitations. However, given our evidence that the majority of increases in woody vegetation extent were not additional (would have happened without the scheme), a NG outcome seems unlikely. The results highlight the importance of considering self-selection bias in the design and evaluation of regulatory biodiversity offsetting policy, and the challenges of conducting robust impact evaluations of jurisdictional biodiversity offsetting policies.

We propose an analytical forecasting framework to quantify the distortionary effects of product-specific subsidies. The novelty consists of a dual approach combining intervention analysis and a state-space modeling approach to construct and assess counterfactuals. Our pure intervention model approach faces challenges in identifying policy effects because, in certain sectors, these are weak and align with the crisis. We complement this approach by fitting a bivariate state-space model excluding the policy and creating counterfactual forecasts for periods of crisis. We then compare intervention analysis estimates of counterfactual series to those of observed series. Subsidizing one industry during global recessions seems unjustified and non-competitive. In our application, we use a time series of foreign and domestic order book levels during and after the temporary installation of an unprecedentedly generous car scrappage scheme in 2009. We assess implied disruptions in the automobile sector and eleven competing manufacturing industries in Germany. We find stimulus effects to be just mild, some evidence of intertemporal payback, and consumers’ windfall gains to come at the expense of other industries.

Abstract This paper proposes an estimation approach following the constructive identification strategy of Athey and Haile, and Gentry and Li with adaption in the context of ascending auctions with selective entry. Our estimators are shown to be consistent in a large sample and to perform well in a finite sample by a simulation study. We apply our estimation approach to the Alibaba online judicial auctions of used cars to recover the bounds of conditional value distribution and the entry cost. The bounds estimates of both conditional value distribution and entry cost are quite tight (resp., relatively wide) for middle‐valued (resp., low‐valued or high‐valued) signal, and the cumulative distribution functions of conditional value distribution given signal comply with the law of ordered dominance. Finally, our counterfactual analysis indicates that (i) the ascending auction yields a higher revenue than the first‐price sealed bid auction, and (ii) the revenue can be improved significantly when the entry cost is cut by half.

Earthquake death tolls are a basic statistical measure of the capability of a country to manage seismic risk. The extremely high Turkish death toll of 50,000 from the Kahramanmaras earthquake doublet of 6 February 2023 is the product of a cascade of detrimental factors. These need to be explained if lessons from this disaster are to be learned. This is the purpose and objective of this paper, which is a contribution to the interdisciplinary Frontiers research topic on integrated perspectives on the 2023 Turkey and Syria earthquakes: advancing understanding and preparedness across earth sciences, engineering and public health. This paper covers these three disciplines by focusing on casualties, and identifying crucial aspects of earth sciences and engineering which contributed to the high death toll. First, there was a surprising combination of multiple fault segment ruptures, and a high level of ground motion relative to the risk-based Turkish code, indicative of the under-representation of the M7.5+ earthquake doublet event in the national probabilistic seismic hazard model. This combination of fault segment ruptures was missing from all seismic source models. Furthermore, the capability of buildings to cope with strong ground motion was much reduced by informal construction methods, which eroded the margin of safety needed to avoid building collapse. The extent of building code non-compliance was widely underestimated in seismic risk models. Noncompliance is often hard to identify, but construction amnesties make noncompliance more transparent and trackable. The disastrous outcome of the Kahramanmaras earthquake doublet of 6 February 2023 has drawn global attention to systemic building code non-compliance, and the open official acceptance of informal housing. To demonstrate that this key systemic risk is far from being just a Turkish problem, notably in Istanbul, the challenge of Italian informal housing is highlighted within the context of international building code non-compliance. [ABSTRACT FROM AUTHOR]

There is growing interest in incorporating metabolomics into public health practice. However, Black women are under-represented in many metabolomics studies. If metabolomic profiles differ between Black and White women, this under-representation may exacerbate existing Black-White health disparities. We therefore aimed to estimate metabolomic differences between Black and White women in the U.S. We leveraged data from two prospective cohorts: the Nurses' Health Study (NHS; n = 2077) and Women's Health Initiative (WHI; n = 2128). The WHI served as the replication cohort. Plasma metabolites (n = 334) were measured via liquid chromatography-tandem mass spectrometry. Observed metabolomic differences were estimated using linear regression and metabolite set enrichment analyses. Residual metabolomic differences in a hypothetical population in which the distributions of 14 risk factors were equalized across racial groups were estimated using inverse odds ratio weighting. In the NHS, Black-White differences were observed for most metabolites (75 metabolites with observed differences ≥ |0.50| standard deviations). Black women had lower average levels than White women for most metabolites (e.g., for N6, N6-dimethlylysine, mean Black-White difference = − 0.98 standard deviations; 95% CI: − 1.11, − 0.84). In metabolite set enrichment analyses, Black women had lower levels of triglycerides, phosphatidylcholines, lysophosphatidylethanolamines, phosphatidylethanolamines, and organoheterocyclic compounds, but higher levels of phosphatidylethanolamine plasmalogens, phosphatidylcholine plasmalogens, cholesteryl esters, and carnitines. In a hypothetical population in which distributions of 14 risk factors were equalized, Black-White metabolomic differences persisted. Most results replicated in the WHI (88% of 272 metabolites available for replication). Substantial differences in metabolomic profiles exist between Black and White women. Future studies should prioritize racial representation. [ABSTRACT FROM AUTHOR]

This paper proposes an estimation approach following the constructive identification strategy of Athey and Haile, and Gentry and Li with adaption in the context of ascending auctions with selective entry. Our estimators are shown to be consistent in a large sample and to perform well in a finite sample by a simulation study. We apply our estimation approach to the Alibaba online judicial auctions of used cars to recover the bounds of conditional value distribution and the entry cost. The bounds estimates of both conditional value distribution and entry cost are quite tight (resp., relatively wide) for middle‐valued (resp., low‐valued or high‐valued) signal, and the cumulative distribution functions of conditional value distribution given signal comply with the law of ordered dominance. Finally, our counterfactual analysis indicates that (i) the ascending auction yields a higher revenue than the first‐price sealed bid auction, and (ii) the revenue can be improved significantly when the entry cost is cut by half. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has caused unprecedented disruptions across industries worldwide. This paper aims to analyze the economic impact of the pandemic on the sales performance of basic commercial areas in Seoul, Korea. Using a regression analysis with credit card transaction data, the study underscores the critical nature of determining the reference point for comparison. Firstly, in comparison to the revenue in the same quarter before the onset of the pandemic, a significant decrease in revenue was observed across most categories during the pandemic periods. Secondly, when compared to the counterfactual revenue in the same period, extrapolated by an exponential smoothing forecasting, the overall revenue decrease during the periods was less pronounced, except in a few categories. Interestingly, certain categories appeared to witness marginal increases in sales after the pandemic. The paper discusses some policy implications of these findings.

Unlicensed cellular networks and spectrum-sharing standards assist operators in meeting the ever-increasing demand for mobile data. However, several incumbents are already operational in these frequencies, rendering the wireless environment extremely dynamic and unpredictable. The challenges associated with unlicensed Licensed Assisted Access (LAA) operations in the 5 GHz band and New Radio in Unlicensed (NR-U) in the 6 GHz band are best addressed through a data-driven approach. This requires operator data from current cellular deployments. Further, from an operator’s perspective, the precision and reliability of predictive models must be analyzed before deployment. Counterfactual machine learning is ideal for quantifying causal impact in a dynamic, unlicensed cellular environment. However, the literature lacks a framework that combines data-driven solutions, counterfactual analysis, and conventional optimization. This work contributes a dataset from the LAA networks of three major cellular operators in Chicago consisting of 15 features and 9676 samples. Additionally, it proposes a framework for analyzing the performance of unlicensed networks that leverages machine learning for predictive modeling, employs counterfactual analysis for model explainability and network performance enhancement, and utilizes optimization for validation. We show that operator data is necessary to build reliable prediction models for network throughput, and signal strength, among others. Further, the impact of network parameters is shown to differ in unlicensed and licensed cellular network models. Next, a counterfactual machine learning framework is proposed to explain and analyze the predictive models. The framework proposes counterfactual policies to enhance unlicensed cellular network performance. Finally, we validate the suggested counterfactual policies through joint network optimization.

• Information-theoretic approach tracks wage gap while capturing inequality in distributions. • Applying this approach to South African data shows that convergence of the gender gap is faster under the entropy measure. • Men's wages stochastically dominate women's wages only for evaluation functions that are sensitive to inequality. • The difference between the male and female wage distributions is largely driven by the wage structure effect. • We argue for the use of inequality sensitive measure when tracking gender wage gap. We examine the trend in the gender wage gap in South Africa taking into consideration the high level of general inequality in the country. To do this, we utilize the information-theoretic approach of the Generalised Entropy (GE) measures of divergence supplemented by stochastic dominance analysis and tests. Under this approach, male and female wage distributions are summarized by suitable evaluation functions and then the difference between the evaluations is computed. Unlike conventional measures of the gender wage gap based on the mean and quantiles, this approach does not assume rank invariance and makes explicit the evaluation functions used. We find that the rate of convergence of the gender wage gap depends on the measure used to aggregate the gap across the entire distribution. Comparison of the aggregate measures of the gender wage gap (mean, median and the entropy measure) reveals that the convergence of the gender wage gap is faster under the entropy measure. This is because unlike the mean, the entropy measure captures the dispersion of wages across individuals. Stochastic dominance test results reveal that male wages dominate female wages mostly in the second-order sense i.e., only for evaluation functions that are increasing in wages and concave. Finally, decomposition results show that across measures, the persistent gender wage gap is attributable to wage structure effects as opposed to composition effects. Therefore, to effectively address the persistent gender wage gap in the middle of the income distribution, policies should be geared towards the returns to characteristics at the median. The implication of these findings is that measures of the gender wage gap that ignore the overall inequality are likely to skew the estimate of the gender wage gap. [ABSTRACT FROM AUTHOR]

Social soft skills are crucial for workers to perform their tasks, yet it is hard to train people on them and to readapt their skill set when needed. In the present work, we analyze the possible effects of the COVID-19 pandemic on social soft skills in the context of Italian occupations related to 88 economic sectors and 14 age groups. We leverage detailed information coming from ICP (i.e. the Italian equivalent of O*Net), provided by the Italian National Institute for the Analysis of Public Policy, from the microdata for research on the continuous detection of labor force, provided by the Italian National Institute of Statistics (ISTAT), and from ISTAT data on the Italian population. Based on these data, we simulate the impact of COVID-19 on workplace characteristics and working styles that were more severely affected by the lockdown measures and the sanitary dispositions during the pandemic (e.g. physical proximity, face-to-face discussions, working remotely). We then apply matrix completion—a machine-learning technique often used in the context of recommender systems—to predict the average variation in the social soft skills importance levels required for each occupation when working conditions change, as some changes might be persistent in the near future. Professions, sectors, and age groups showing negative average variations are exposed to a deficit in their social soft-skills endowment, which might ultimately lead to lower productivity. [ABSTRACT FROM AUTHOR]

Using a balanced panel of local labour market areas (LLMAs) and adopting a new difference-in-differences approach with multiple time periods and multiple groups, this paper assesses the causal effects on local labour market outcomes of the earthquakes that occurred in Italy in 2009, 2012 and 2016. The results show a strong heterogeneity: the 2009 event had significant negative and persistent impacts on the employment rate of the LLMAs involved, while there were no adverse effects for the LLMAs affected by the earthquakes in 2012 and 2016. We also extend the analysis at the industry level in order to highlight the sectoral shifts that typically characterise the aftermath of these natural disasters. [ABSTRACT FROM AUTHOR]

This paper contributes to the literature examining the impact of large-scale sport events (LSEs) on international tourist inflows from 1995 to 2019. Our proposed method involves the calculation of tourist potential and the analysis of actual-to-potential ratios before and after the event. This approach allows us to measure the effect of LSE on a particular hosting country rather than its average effect. Our findings demonstrate a strong "crowding-out" effect for international tourists, with only four out of 18 LSEs resulting in a positive effect on tourist arrivals in the host country. [ABSTRACT FROM AUTHOR]

Carbon trading markets are crucial policy instruments in carbon emission reduction and carbon neutrality. Yet, China's pilot programs encounter diverse operational modes and environmental factors that might impact their effectiveness. This study uses panel data from 30 provinces (2000–2019) in China and the regression control method to evaluate and analyze the heterogeneous effects of carbon trading pilots (CTPs) on emission reduction. Results reveal three types of CTP effects which are as follows: reducing both total carbon emissions (TCEs) and carbon intensity (CI) as noticed in Shanghai; decreasing CI while increasing TCE as seen in Beijing, Tianjin, Guangdong, and Hubei; and raising both TCE and CI as observed in Chongqing and Fujian. Significantly, market mechanisms in carbon pricing and state intervention, including of state-owned enterprises, play notable roles in these effects. Furthermore, CTP policies display both intensity reduction and energy rebound effects; the direction of carbon emission reduction relies on the balance between these effects. The findings offer empirical support to enhance carbon market effectiveness and provide valuable insights for regions in China and globally in order to tailor policies based on their specific conditions. [ABSTRACT FROM AUTHOR]

Earthquake death tolls are a basic statistical measure of the capability of a country to manage seismic risk. The extremely high Turkish death toll of 50,000 from the Kahramanmaraş earthquake doublet of 6 February 2023 is the product of a cascade of detrimental factors. These need to be explained if lessons from this disaster are to be learned. This is the purpose and objective of this paper, which is a contribution to the interdisciplinary Frontiers research topic on integrated perspectives on the 2023 Turkey and Syria earthquakes: advancing understanding and preparedness across earth sciences, engineering and public health. This paper covers these three disciplines by focusing on casualties, and identifying crucial aspects of earth sciences and engineering which contributed to the high death toll. First, there was a surprising combination of multiple fault segment ruptures, and a high level of ground motion relative to the risk-based Turkish code, indicative of the under-representation of the M7.5+ earthquake doublet event in the national probabilistic seismic hazard model. This combination of fault segment ruptures was missing from all seismic source models. Furthermore, the capability of buildings to cope with strong ground motion was much reduced by informal construction methods, which eroded the margin of safety needed to avoid building collapse. The extent of building code non-compliance was widely underestimated in seismic risk models. Non-compliance is often hard to identify, but construction amnesties make non-compliance more transparent and trackable. The disastrous outcome of the Kahramanmaraş earthquake doublet of 6 February 2023 has drawn global attention to systemic building code non-compliance, and the open official acceptance of informal housing. To demonstrate that this key systemic risk is far from being just a Turkish problem, notably in Istanbul, the challenge of Italian informal housing is highlighted within the context of international building code non-compliance.

Introduction. The implementation of the decree of the President of the Russian Federation is aimed at ensuring the food security of the country and requires the industrialization of the agro-industrial sector. The effectiveness of industrialization depends on the use of automated, intelligent solutions at all stages of implementing technological processes. Livestock is an agro-industrial sector generating the largest amount of organic waste materials, which are potential energy carriers: litter, liquid manure, process effluents, etc. According to the data from the Russian Statistics Committee and the research results, the annual volume of manure generated from farms is from 43.3 to 45.1 million tons, while there is an upward trend. The used energy potential from the entire volume does not exceed 40%. It is possible to increase the efficiency of using the energy potential of organic animal waste materials through implementing digitalized solutions. A strategic tool for the effective industrialization of livestock is the implementation of application software products that ensure the growth of ecological and energy effects. Aim of the Article. The aim of the study is a counterfactual evaluation of the efficiency of the model for decontaminating liquid pig manure in the decontamination activator. Materials and Methods. Counterfactual analysis is a tool for formalizing complex, multifactorial processes to ensure their subsequent digitalization. The essence of the analysis consists in a “surveyˮ of the analyzed model through which the values of variables are determined providing changes that lead to a deviation of the response beyond the boundary conditions during interpretation. The advantage of counterfactual analysis is the stability and transparency of the model to external influences during machine learning. It is known that the representative pathogenic markers of the decontamination efficiency of liquid pig manure are helminth eggs and the number of colony-forming units of common coliform bacteria (CFU CCB). However, for testing and implementing an algorithm for counterfactual analysis of a mathematical model, it is acceptable to use the number of CFU CCB. The object of the study was liquid pig manure with a humidity from 88% to 98%, the subject was a counterfactual analysis of the dependence of the number of CFU CCB on the exposure time in the activator, the concentration of active chlorine, the mass of working bodies, magnetic induction, and liquid manure humidity. Results. The results of counterfactual evaluation and analysis carried with the use of the Python programming language and the PyCharm 2022.2 environment are presented in the tables. The counterfactual evaluation made it possible to identify ranges of variation of factors, the use of which can represent the potential of boundary conditions in solving the optimization problem. The cells of these values are highlighted in grey-blue. The most preferred ranges based on counterfactual evaluation are in the cells highlighted in green. Discussion and Conclusions. There has been substantiated the prospects of using active chlorine in combination with the influence of ferromagnetic working bodies moving in an alternating rotating electromagnetic field as a decontamination activator. On the basis of counterfactual evaluation it was established that the most significant factors for determining the efficiency of decontamination of liquid pig manure by the number of CFU CCB are: magnetic induction in the working zone of the activator inductor, active chlorine concentration and exposure time.

The Lessons from Covid-19 Research Agenda offers a structure to study the COVID-19 pandemic and the pandemic response from a Global Catastrophic Risk (GCR) perspective. The agenda sets out the aims of our study, which is to investigate the key decisions and actions (or failures to decide or to act) that significantly altered the course of the pandemic, with the aim of improving disaster preparedness and response in the future. It also asks how we can transfer these lessons to other areas of (potential) global catastrophic risk management such as extreme climate change, radical loss of biodiversity and the governance of extreme risks posed by new technologies. Our study aims to identify key moments- ‘inflection points’- that significantly shaped the catastrophic trajectory of COVID-19. To that end this Research Agenda has identified four broad clusters where such inflection points are likely to exist: pandemic preparedness, early action, vaccines and non-pharmaceutical interventions. The aim is to drill down into each of these clusters to ascertain whether and how the course of the pandemic might have gone differently, both at the national and the global level, using counterfactual analysis. Four aspects are used to assess candidate inflection points within each cluster: 1. the information available at the time; 2. the decision-making processes used; 3. the capacity and ability to implement different courses of action, and 4. the communication of information and decisions to different publics. The Research Agenda identifies crucial questions in each cluster for all four aspects that should enable the identification of the key lessons from COVID-19 and the pandemic response.

The impact of autonomous vehicles on the spatial size of cities remains ambiguous, as the future is highly uncertain. This paper uses counterfactual analysis techniques to examine the effects of autonomous vehicles on urban expansion for metropolitan areas in the United States if autonomous vehicles had been introduced before. We argue that distance cost and congestion cost, which are the two components of transportation cost with different effects on urban expansion, should be addressed in autonomous vehicle research. By coupling historical data with hypothetical scenarios of introducing autonomous vehicles to cities, we find that urban expansion, rather than urban densification, would have been the dominant effect if autonomous vehicles had been introduced into cities. The finding indicates that if autonomous vehicles are widely adopted in the future, they are likely to have similar, or even larger, effects on future urban expansion than in the counterfactual past. [ABSTRACT FROM AUTHOR]

Urban airports are typically large infrastructures with significant cultural, economic, and ecological impacts; meanwhile, abandoned airports are common worldwide. However, there is limited knowledge regarding transformation strategies for the renewal of abandoned airports and their surrounding regions in historically and culturally rich areas. We use Beijing's Nanyuan Airport as a case study, combining the historic urban landscape approach, land use and land cover change, and counterfactual simulations of land use patterns to construct a comprehensive analytical framework. Our framework was used to analyze the long-term land use patterns of the study area, determine its value, and improve perception from a macro- and multi-perspective. We discovered that the traditional knowledge and planning systems in the study area have largely disappeared, but Nanyuan Airport's impact on the surrounding land use patterns is unique and significant. By considering the characteristics and mechanisms of land use in the study area, we aimed to find a balance point between the historical context and future potential. As such, we propose optimized recommendations with the theme of connection and development engines. Our findings supplement the planning knowledge of relevant areas and provide a springboard for interdisciplinary research in landscape planning. [ABSTRACT FROM AUTHOR]

Enthusiasm for "greening the financial system" is welcome, but a fundamental challenge remains: financial decision makers lack the necessary information. It is not enough to know that climate change is bad. Markets need credible, digestible information on how climate change translates into material risks. To bridge the gap between climate science and real-world financial indicators, we simulate the effect of climate change on sovereign credit ratings for 109 countries, creating the world's first climate-adjusted sovereign credit rating. Under various warming scenarios, we find evidence of climate-induced sovereign downgrades as early as 2030, increasing in intensity and across more countries over the century. We find strong evidence that stringent climate policy consistent with limiting warming to below 2 °C, honoring the Paris Climate Agreement and following representative concentration pathway (RCP) 2.6, could nearly eliminate the effect of climate change on ratings. In contrast, under higher emissions scenarios (i.e., RCP 8.5), 59 sovereigns experience climate-induced downgrades by 2030, with an average reduction of 0.68 notches, rising to 81 sovereigns facing an average downgrade of 2.18 notches by 2100. We calculate the effect of climate-induced sovereign downgrades on the cost of corporate and sovereign debt. Across the sample, climate change could increase the annual interest payments on sovereign debt by US$45–$67 billion under RCP 2.6, rising to US$135–$203 billion under RCP 8.5. The additional cost to corporations is US$10–$17 billion under RCP 2.6 and US$35–$61 billion under RCP 8.5. This paper was accepted by Colin Mayer, Special Section of Management Science on Business and Climate Change. Funding: M. Agarwala, P. Klusak, and M. Burke acknowledge funding from the International Network for Sustainable Financial Policy Insights, Research and Exchange (INSPIRE). M. Agarwala also acknowledges funding from The Wealth Economy Project. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4869. [ABSTRACT FROM AUTHOR]

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Summary: This paper proposes a direct approach to Kilian–Lewis style counterfactual analysis in structural vector autoregression models. The proposed approach is easy to implement, and the procedure of counterfactual construction is more transparent than the indirect approach. Practitioners conducting Kilian–Lewis style counterfactual analysis should find the proposed approach novel and useful. [ABSTRACT FROM AUTHOR]

Abstract Dyadic data is often encountered when quantities of interest are associated with the edges of a network. As such it plays an important role in statistics, econometrics and many other data science disciplines. We consider the problem of uniformly estimating a dyadic Lebesgue density function, focusing on nonparametric kernel-based estimators taking the form of dyadic empirical processes. Our main contributions include the minimax-optimal uniform convergence rate of the dyadic kernel density estimator, along with strong approximation results for the associated standardized and Studentized t-processes. A consistent variance estimator enables the construction of valid and feasible uniform confidence bands for the unknown density function. We showcase the broad applicability of our results by developing novel counterfactual density estimation and inference methodology for dyadic data, which can be used for causal inference and program evaluation. A crucial feature of dyadic distributions is that they may be “degenerate” at certain points in the support of the data, a property making our analysis somewhat delicate. Nonetheless our methods for uniform inference remain robust to the potential presence of such points. For implementation purposes, we discuss inference procedures based on positive semi-definite covariance estimators, mean squared error optimal bandwidth selectors and robust bias correction techniques. We illustrate the empirical finite-sample performance of our methods both in simulations and with real-world trade data, for which we make comparisons between observed and counterfactual trade distributions in different years. Our technical results concerning strong approximations and maximal inequalities are of potential independent interest. [ABSTRACT FROM AUTHOR]

Background: Chronic kidney disease (CKD) poses a major global public health burden, with over 700 million affected. Early identification of those in whom the disease is likely to progress enables timely therapeutic interventions to delay advancement to kidney failure. Methods: This study developed explainable machine learning models leveraging pathology data to accurately predict CKD trajectory, targeting improved prognostic capability even in early stages using limited datasets. Key variables used in this study include age, gender, most recent estimated glomerular filtration rate (eGFR), mean eGFR, and eGFR slope over time prior to the incidence of kidney failure. Supervised classification modelling techniques included decision tree and random forest algorithms selected for interpretability. Internal validation on an Australian tertiary centre cohort (n = 706; 353 with kidney failure and 353 without) achieved exceptional predictive accuracy. To address the inherent class imbalance, centroid-cluster-based under-sampling was applied to the Australian dataset. For external validation, the model was applied to a dataset (n = 597 adults) sourced from a Japanese CKD registry. Transfer learning was subsequently employed by fine-tuning machine learning models on 15 % of the external dataset (n = 89) before evaluating the remaining 508 patients. Results: Internal validation achieved exceptional predictive accuracy, with the area under the receiver operating characteristic curve (ROC-AUC) reaching 0.94 and 0.98 on the binary task of predicting kidney failure for decision tree and random forest, respectively. External validation demonstrated performant results with an ROC-AUC of 0.88 for the decision tree and 0.93 for the random forest model. Decision tree model analysis revealed the most recent eGFR and eGFR slope as the most informative variables for prediction in the Japanese cohort. Conclusion: The research highlights the utility of deploying explainable machine learning techniques to forecast CKD trajectory even in the early stages utilising limited real-world datasets.

Purpose – The purpose of this paper is to estimate the impact of the 2008 and 2020 economic crises on employment in Spain. Design/methodology/approach – The authors perform a counterfactual analysis, combining intervention (interrupted time series) analysis and conditional forecasting to estimate a “crisis-free” scenario. These counterfactual estimates are used as a synthetic control, to be compared with the observed values of the main variables of the Spanish Labor Force Survey (EPA). Findings – The authors measure the effect on Spanish employment of the 2008 recession and the ongoing COVID/Ukraine crisis and the speed of recovery, which yields a rigorous dating for the beginning and end of the crises studied. Finally, the authors provide estimates about which part of the employed and unemployed people was in furlough (ERTE) based on microdata provided by the Spanish Institute of Statistics. Originality/value – To the best of the authors’ knowledge, there are no counterfactual studies covering all the basic variables in EPA and no estimates for the effect of ERTEs on the basic employment variables. Finally, the authors combine well-known intervention and forecasting techniques into an integrated framework to assess the effects of both, past and ongoing crises.

Counterfactual analysis, which compares the competitive situation prevailing with and without the allegedly abusive behaviour, is nowadays regarded the lynchpin of an effects-based approach to the analysis of dominant firm conduct under Article 102 TFEU. This article draws on the recent Qualcomm ruling by the General Court of the European Union to critically reflect on the use and requirement of counterfactual analysis in abuse of dominance cases. It argues that Qualcomm offers two lessons on the role of the counterfactual analysis in modern competition law. First, it shows that counterfactual analysis is vulnerable to under-inclusiveness and type II errors when it ignores the problem of concurrent causes of foreclosure effects, disregards standard economic analysis of exclusivity rebates, and remains oblivious to dynamic competition. Second, Qualcomm sheds light on the intricate relationship between the counterfactual analysis and the requisite standard of harm for finding anticompetitive effects under Article 102 TFEU. In limiting the relevant counterfactual scenarios to a very narrow set of actual or nearby likely worlds, Qualcomm is but the last indication of a much more profound transformation of Article 102 TFEU: the transition from a capability to a balance of probabilities or beyond reasonable doubt standard of harm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Social Sciences & Philosophy is the property of Research Center for Humanities & Social Sciences, Academia Sinica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Biodiversity offsetting is a globally influential policy mechanism for reconciling trade‐offs between development and biodiversity loss. However, there is little robust evidence of its effectiveness. We evaluated the outcomes of a jurisdictional offsetting policy (Victoria, Australia). Offsets under Victoria's Native Vegetation Framework (2002–2013) aimed to prevent loss and degradation of remnant vegetation, and generate gains in vegetation extent and quality. We categorised offsets into those with near‐complete baseline woody vegetation cover ("avoided loss", 2702 ha) and with incomplete cover ("regeneration", 501 ha), and evaluated impacts on woody vegetation extent from 2008 to 2018. We used two approaches to estimate the counterfactual. First, we used statistical matching on biophysical covariates: a common approach in conservation impact evaluation, but which risks ignoring potentially important psychosocial confounders. Second, we compared changes in offsets with changes in sites that were not offsets for the study duration but were later enrolled as offsets, to partially account for self‐selection bias (where landholders enrolling land may have shared characteristics affecting how they manage land). Matching on biophysical covariates, we estimated that regeneration offsets increased woody vegetation extent by 1.9%–3.6%/year more than non‐offset sites (138–180 ha from 2008 to 2018) but this effect weakened with the second approach (0.3%–1.9%/year more than non‐offset sites; 19–97 ha from 2008 to 2018) and disappeared when a single outlier land parcel was removed. Neither approach detected any impact of avoided loss offsets. We cannot conclusively demonstrate whether the policy goal of 'net gain' (NG) was achieved because of data limitations. However, given our evidence that the majority of increases in woody vegetation extent were not additional (would have happened without the scheme), a NG outcome seems unlikely. The results highlight the importance of considering self‐selection bias in the design and evaluation of regulatory biodiversity offsetting policy, and the challenges of conducting robust impact evaluations of jurisdictional biodiversity offsetting policies. [ABSTRACT FROM AUTHOR]

During the first year of the COVID-19 pandemic, governments only had access to non-pharmaceutical interventions (NPIs) to mitigate the spread of the disease. Various methods have been discussed in the literature for calculating the effectiveness of NPIs. Among these methods, the interrupted time series analysis method is the area of our interest. To study the second wave, we clustered countries based on levels of implemented NPIs, except for the target NPI (X) whose effectiveness wanted to be evaluated. To do so, the COVID-19 Policy Response Tracker data-set gathered by the "Our World in Data" team of Oxford University, and COVID-19 statistical data gathered by the John Hopkins Hospital were used. After clustering, we selected a counterfactual country from the countries that were in the same cluster as the target country, and implemented NPI (X) at its lowest level. Thus, the target country and the counterfactual country were similar in implementation level of other NPIs and only differed in the implementation level of the target NPI (X). Therefore, we can calculate the effectiveness of NPI (X) without being concerned about the impurity of the effectiveness values that might be caused by other NPIs. This allowed us to calculate the effectiveness of NPI (X) using the interrupted time series analysis with the control group. Interrupted time series analysis assesses the effect of different policy-implementation levels by evaluating interruptions caused by policies in trend and level after the policy-implementation date. Before the NPI-implementation date, the implementation levels of NPIs were similar in both selected countries. After this date, the counterfactual country could be treated as a baseline for calculating changes in the trends and levels of COVID-19 cases in the target country. To demonstrate this approach, we used the generalized least square (GLS) method to estimate interrupted time series parameters related to the effectiveness of school closure (the target NPI) in Spain (the target country). The results show that increasing the implementation level of school closure caused a 34% decrease in COVID-19 prevalence in Spain after only 10 days compared to the counterfactual country. [ABSTRACT FROM AUTHOR]

The paper aims to explore alternative statehood options for Yugoslavia's unification in 1918 and assess their potential success for Serbian interests. It falls within the realm of counterfactual analysis. The study draws on the economic theory of optimal nation size, which examines the pros and cons of larger nations, particularly the challenges posed by cultural diversity resulting from ethnolinguistic and religious differences. Five statehood alternatives have been identified, involving various degrees of Serbia's territorial expansion. Empirical evidence from the 1921 population census demonstrates that options leading to larger nation size would increase population heterogeneity. The research identifies an optimal solution that balances benefits and costs. [ABSTRACT FROM AUTHOR]

Copyright of Annals of the Faculty of Law in Belgrade is the property of University of Belgrade, Faculty of Law and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Following the 2007–08 multiple crises, land-rich nations witnessed an unprecedented rise in land grabbing by different actors. However, there is a lack of evidence for the impact of land grabbing by actors even though such information could be very useful for development, theoretical and empirical studies. This study investigates the food security (i.e., household income food consumption score, household food insecurity access score, and self-reported food security) impacts of land grabbing by domestic (LGDE) and foreign entities (LGFE) in Ghana, using a farm household-level data from a survey conducted during the 2017/2018 cropping season. Specifically, we test two main hypotheses on food security impacts of LGDE and LGFE: (i) given that land grabbing leads to farmland loss and fewer job opportunities, food security will decrease under both LGDE and LGFE; and (ii) the decrease in food security will be higher under LGFE as compared to LGDE since foreign actors dominate in land grabbing, and control more land than domestic actors. The multinomial endogenous switching regression and multinomial endogenous treatment effect models were applied to control for potential selection bias and to test these hypotheses. The results revealed that a decrease in food security ranged between 15 and 61% for LGDE, and 11% and 49% for LGFE. These suggest that both LGDE and LGFE decrease food security but the decrease is much associated with LGDE as compared to LGFE. Thus, concerns about the negative impacts of land grabbing should be directed to all actors in land grabbing. The findings also have implications for development, theoretical and empirical studies which are presented and discussed. [ABSTRACT FROM AUTHOR]

The paper aims to explore alternative statehood options for Yugoslavia's unification in 1918 and assess their potential success for Serbian interests. It falls within the realm of counterfactual analysis. The study draws on the economic theory of optimal nation size, which examines the pros and cons of larger nations, particularly the challenges posed by cultural diversity resulting from ethnolinguistic and religious differences. Five statehood alternatives have been identified, involving various degrees of Serbia's territorial expansion. Empirical evidence from the 1921 population census demonstrates that options leading to larger nation size would increase population heterogeneity. The research identifies an optimal solution that balances benefits and costs.

Access to affordable, reliable, and modern energy remains a critical goal under the Agenda 2030 for Sustainable Development, especially in remote areas of developing countries. Based on traditional engineering approaches, many energy solution planning tools have been developed to identify the optimal solution in these areas to assess the competition across different technological options. Nevertheless, these approaches, based on an economic optimum, do not necessarily grant long-term sustainability of the solution in specific local contexts, since they are not able to capture the social implications within the Energy-Development nexus. Moreover, also in light of the 2030 Agenda, scientific and grey literature on energy access highlights how energy solutions planning methodologies developed in the last decades need to be complemented by a more comprehensive view, able to integrate evidence from various disciplines, especially engineering and social sciences. Based on the above considerations, this paper introduces a novel framework under the name of CESP, where three social sciences-based phases complement three engineering phases, each one characterized by specific tools, to offer an informed decision framework for the local planner. CESP encompasses a set of techno-economic and socio-technical actions to prevent potential failure as evidenced by a counterfactual analysis used to identify the reasons behind past project failures. The CESP framework presents a sequential and iterative structure that underlines the cyclic perspective of a holistic decision process where social sciences feed the engineering analysis and vice versa. Finally, CESP emerges as a practical and applicable framework for supporting energy access planning in critical areas.