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.

COVID-19 has posed unprecedented challenges to global health and the world economy. Two years into the pandemic, the widespread impact of COVID-19 continues to deepen, impacting different industries such as the automotive industry and its supply chain. This study presents a hybrid approach combining simulation modeling and tree-based supervised machine learning techniques to explore the implications of end-market demand disruptions. Specifically, we apply the concept of born-again tree ensembles, which are powerful and, at the same time, easily interpretable classifiers, to the case of the semiconductor industry. First, we show how to use born-again tree ensembles to explore data generated by a supply chain simulation model. To this end, we demonstrate the influence of varying behavioral and structural parameters and show the impact of their variation on specific key performance indicators, e.g., the inventory level. Finally, we leverage a counterfactual analysis to identify detailed managerial insights for semiconductor companies to mitigate adverse impacts on one echelon or the entire supply chain. Our hybrid approach provides a simulation model enhanced by a tree-based supervised machine learning model that companies can use to determine optimal measures for mitigating the adverse effects of end-market demand disruptions. We close the loop of our analysis by integrating the findings of the counterfactual analysis backward into the simulation model to understand the overall dynamics within the multi-echelon supply chain.

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.

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.

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.

This paper analyzes the impact of automatic wage indexation on employment. To boost competitiveness and increase employment, Belgium suspended its automatic wage indexation system in 2015. This resulted in a 2% fall in real wages for all workers. In the absence of a suitable control group, we use machine learning for the counterfactual analysis. We artificially construct the control group for a difference-in-difference analysis based on the pre-treatment evolution of treated firms. We find a positive impact on employment of 1.2%, which corresponds to a labor demand elasticity of - 0.6. This effect is more pronounced for manufacturing firms, where the elasticity reaches - 1. These results show that a suspension of the automatic wage indexation mechanism can be effective in preserving employment.

COVID-19 scenarios were run using an epidemiological mathematical model (system dynamics model) and counterfactual analysis to simulate the impacts of different control and containment measures on cumulative infections and deaths in Bangladesh and Pakistan. The simulations were based on national-level data concerning vaccination level, hospital capacity, and other factors, from the World Health Organization, the World Bank, and the Our World in Data web portal. These data were added to cumulative infections and death data from government agencies covering the period from 18 March 2020 to 28 February 2022. Baseline curves for Pakistan and Bangladesh were obtained using piecewise fitting with a consideration of different events against the reported data and allowing for less than 5% random errors in cumulative infections and deaths. The results indicate that Bangladesh could have achieved more reductions in each key outcome measure by shifting its initial lockdown at least five days backward, while Pakistan would have needed to extend its lockdown to achieve comparable improvements. Bangladesh\'s second lockdown appears to have been better timed than Pakistan\'s. There were potential benefits from starting the third lockdown two weeks earlier for Bangladesh and from combining this with the fourth lockdown or canceling the fourth lockdown altogether. Adding a two-week lockdown at the beginning of the upward slope of the second wave could have led to a more than 40 percent reduction in cumulative infections and a 35 percent reduction in cumulative deaths for both countries. However, Bangladesh\'s reductions were more sensitive to the duration of the lockdown. Pakistan\'s response was more constrained by medical resources, while Bangladesh\'s outcomes were more sensitive to both vaccination timing and capacities. More benefits were lost when combining multiple scenarios for Bangladesh compared to the same combinations in Pakistan. Clearly, cumulative infections and deaths could have been highly impacted by adjusting the control and containment measures in both national settings. However, COVID-19 outcomes were more sensitive to adjustment interventions for the Bangladesh context. Disaggregated analyses, using a wider range of factors, may reveal several sub-national dynamics. Nonetheless, the current research demonstrates the relevance of lockdown timing adjustments and discrete adjustments to several other control and containment measures.

UNASSIGNED: Adverse childhood experience is thought to be associated with risk of coronary heart disease, but it is not clear which experiences are cardiotoxic, and whether risk increases with the accumulation of adverse childhood experiences.
UNASSIGNED: Participants were 5149 adults (72.6% men) in the Whitehall II cohort study. Parental death was recorded at phase 1 (median age in years 44.3), and 13 other adverse childhood experiences at phase 5 (55.3). We applied Cox proportional hazards regression with person-time from phase 5 to examine associations of adverse childhood experiences with incident coronary heart disease. We predicted hazard ratios according to count of the experiences, and examined dose-response effect. We finally estimated reduction of coronary heart disease in a hypothetical scenario, the absence of adverse childhood experiences.
UNASSIGNED: Among study participants, 62.9% had at least one adversity, with \"financial problems\" having the highest prevalence (26.1%). There were 509 first episodes of coronary heart disease during an average 12.9 years follow-up. Among 14 adverse childhood experiences in a multiply adjusted model, \"parental unemployment\" showed the highest hazard of coronary heart disease incidence (hazard ratio; 95% confidence interval: 1.53; 1.16 to 2.02). No dose-response effect was observed (constant for proportionality in hazard ratio: 1.05, 0.99 to 1.11). Based on the estimates of final model, in the absence of childhood adversities, we estimated a 6.0% reduction in coronary heart disease (0.94; 0.87 to 1.01), but the confidence interval includes one.
UNASSIGNED: Although individual adverse childhood experiences show some association with coronary heart disease, there is no clear relationship with the number of adverse experiences. Further research is required to quantify effects of multiple and combinations of adverse childhood experiences considering timing, duration, and severity.

After decades of research on traffic conflicts and other crash surrogate events, defining these events and conclusively connecting them with crashes continue to be the most important tasks. This paper aims to help establish a consensus on these two fundamental matters by discussing the underlying concepts by which they can be connected in a consistent construct justified with theory and empirical evidence. The importance of insight into a safety-relevant event beyond what is externally observable is emphasized by considering two distributions of crash nearness: (1) values observed by external observers and (2) driver-preferred values that are usually unobservable. Traffic encounters and traffic conflicts are discussed here in the context of crash possibility illustrated with these two distributions. The difference between the preferred and observed crash nearness values is introduced as the delay of response to an error that violates the crash nearness preference. Traffic conflicts caused by driver errors that violate the driver populace\'s minimum crash nearness are recommended for safety analysis if only external observations are available. The conditions of properly detecting such traffic conflicts and estimating the probability of crash are identified and their validity is emphasized based on the past SHRP2 study. The mentioned study identified two additional conditions for proper identification of traffic conflicts: (1) speeds sufficiently high to induce driver responses consistent with the theory and with Lomax distribution and (2) elimination of self-clearing encounters such as a preceding vehicle exiting a lane in rear-end interactions. The most encouraging finding of this study is the mentioned sufficiently high speeds that tend to coincide with collision outcomes sufficiently serious to be reportable to the authorities. Another encouraging element is the insight about preferred crash nearness values that may be brought by autonomous vehicles. The biggest challenge in applying EV modeling today is using proper safety-relevant events to ensure that the tail of a distribution estimated based on observed events is consistent with the distribution tail that represents a crash. Autonomous vehicles may help eliminate this challenge since their preferred crash nearness values should be known.

Although the frequency and severity of crashes are direct measures of road safety, crash data are typically of limited quality and they require long data collection periods to produce conclusive results. Surrogates of crashes that would allow a quick and accurate estimation of safety have been an active topic for years. Among multiple alternatives, traffic conflicts have been established as a promising surrogate measure. This paper is aimed to demonstrate the validity of failure-caused traffic conflicts by applying a recently proposed Lomax-based method to estimate the expected number of crashes from observed traffic conflicts. The data collected in the naturalistic driving program, the Second Strategic Highway Research Program (SHRP2), were used in the validation task. The rear-end crashes recorded during the SHRP2 program and the corresponding rear-end traffic conflicts were analyzed for three categories of drivers: young male, mature male, and mature female. Past research has indicated that these three categories have a distinctively different proneness to involvement in crashes. Out of all rear-end traffic conflicts included in the SHRP2 database, 1.4 % were used to estimate the crash frequencies and rates for each studied type of driver. The Lomax distribution was applied within the counterfactual framework. Then, the conflict-based crash rate estimates were compared to the crash rates of the studied types of drivers calculated from all the rear-end crashes observed in the SHRP2 study period. The conflict-based rate estimates followed well the crash-based rates and the existing knowledge about the safety performance of the studied drivers. The conflict-based results confirmed the over-representation of young male drivers in crashes. It was also confirmed that mature male drivers are involved in rear-end crashes more frequently than mature female drivers. The results demonstrate both the validity of the Lomax-based analysis of failure-caused traffic conflicts and the benefit of traffic conflicts analysis that considerably reduces a period of data collection from years for crashes to days or weeks for traffic conflicts.