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In most countries, the private sector owns the vast majority of the buildings and a considerable portion of the infrastructure at risk. However, most investment in disaster risk management is made by the public sector, with the private sector lagging far behind. The situation represents missed opportunities for businesses to capture not only higher levels of the direct benefits of disaster risk management, but also a broader set of co-benefits to themselves and society as a whole. These co-benefits include ways of lowering production costs, improving the health of workers, and contributing to general economic stability. Ironically, many of these co-benefits are more tangible and immediate than ordinary disaster risk management benefits, which may not appear until a disaster has struck many years after the investment has been made. This study analyzes several important facets of private sector investment in disaster risk management, primarily from an economic perspective. It is intended as a first step toward promoting greater investment in disaster risk management by identifying potential co-benefits, explaining why they are not always pursued, and suggesting ways to integrate them into private sector decision-making. The latter includes government incentives, justified on the grounds that many private sector investments have extensive co-benefits, many of which pay dividends to society as a whole.

Co-Benefits --- Disaster Risk Management --- Market Failure --- Private Sector --- Spillover Effects

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The Volkswagen emissions scandal of 2015 led to numerous financial consequences at
different levels and for a substantial variety of stakeholders. This event therefore opened the
way for a large research field. As most of the previous published papers investigating it focused
on an intra-sectoral analysis, this thesis aims to assess the potential cross-sectoral spillover
effects on the stock market caused by the event. In other words, the point of this research is to
determine, based on the case of this scandal, whether an environmental misconduct in one
industry engenders spillover effects on other external industries (which are potentially harmful
for the environment). After briefly reminding the reader of the context of this event and
covering the existing literature about the issue, a first hypothesis tries to answer this research
question, using a GARCHX methodology with a dummy variable representing the event days
of the scandal. In the models, each sector is introduced by a global index.
Given the not significant results of this first analysis, a second hypothesis is investigated
thanks to a VAR model and a Granger causality test. It questions the potential importance of
the closeness of the business activities of the analyzed sectors when implementing such a crosssectoral spillovers research. Again, no significant impact was found on these new sectors
supposed to be closer to the automotive industry.
The main conclusions then suggest that an environmental corporate misconduct in one
sector does not affect stock markets of external sectors, no matter the closeness they have with
the event announcing sector. Nevertheless, it is important to remind that, to confirm that these
findings may be applied to all events similar to the Volkswagen scandal, such a research should
be carried out using a wider sample of events rather than focusing on one only.

Spillover effects --- event study --- Dieselgate --- GARCH --- VAR --- Sciences économiques & de gestion > Finance

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This paper is a practical guide for researchers and practitioners who want to understand spillover effects in program evaluation. The paper defines spillover effects and discusses why it is important to measure them. It explains how to design a field experiment to measure the average effects of the treatment on eligible and ineligible subjects for the program in the presence of spillover effects. In addition, the paper discusses the use of nonexperimental methods for estimating spillover effects when the experimental design is not a viable option. Evaluations that account for spillover effects should be designed such that they explain the cause of these effects and whom they affect. Such an evaluation design is necessary to avoid inappropriate policy recommendations and neglecting important mechanisms through which the program operates.

Data Collection --- Disease Control & Prevention --- Field Experiments --- Impact Evaluation --- Indirect Treatment Effect --- Labor Policies --- Population Policies --- Program Mechanisms --- Science Education --- Scientific Research & Science Parks --- Spillover Effects

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This paper reviews the experiences of intergovernmental fiscal systems (IGFS) to look for possible lessons for how Official Development Assistance (ODA) is delivered. Specifically, it compares IGFS and ODA in two specific respects. The first is the proportion of public resources that central governments make available to sub-national governments on a conditional basis, and how that compares with the proportion of non-earmarked ODA given to low income countries. The second is the role of performance outcome in resource allocation to sub-national governments and how that compares with the role of performance in ODA, particularly multilateral ODA allocation. The comparisons show that: (i) the share of earmarked ODA is more than three times higher than that of conditional grants in intergovernmental systems, suggesting that donors in ODA rely more on earmarking to influence the spending decisions of the recipient countries than their federal governments do on conditional grants to induce policy changes in their own sub-national governments; and (ii) none of the OECD countries currently use outcome measures in determining resource allocation to their sub-national governments for a variety of good reasons, and the recent debate as to whether multilateral ODA allocation should be based on development outcomes seems to ignore this experience.

Administrative Costs --- Audits --- Autonomy --- Capacity Building --- Capital Markets and Capital Flows --- Debt --- Debt Markets --- Decentralization --- Developing Countries --- Development Economics & Aid Effectiveness --- Economic Development --- Employment --- Expenditures --- Finance and Financial Sector Development --- Financial Crisis --- Fiscal Sustainability --- Immunizations --- Macroeconomics and Economic Growth --- Multilateral Development Banks --- Natural Disasters --- Public & Municipal Finance --- Public Finance --- Public Sector --- Spillover Effects --- Statistical analysis --- Subnational Governments --- Tax Policy --- Transparency --- Uncertainty

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Recent developments in the natural gas industry warrant new analysis of related issues. Environmental, social, and governance (ESG) investments have accelerated the shift away from coal as the dominant source of electricity. Its low environmental impact, reduced volume, and broad availability make liquefied natural gas (LNG) a popular alternative, during this time of transition between traditional fuels and newer options. In the United States, the shale gas revolution has made natural gas a game changer. In this book, we focus on empirical analyses of the natural gas market and its growing relevance worldwide.

spillover effect --- market integration --- natural gas market --- time frequency dynamics --- BRICS --- exchange rates --- connectedness --- time domain --- frequency domain --- natural gas --- crude oil --- electricity utilities sector index --- time–frequency dynamics --- ESG --- renewable energy --- copula --- value-at-risk --- electricity --- spot --- futures --- transmission --- pipelines --- external cost --- health --- property damage --- bodily injury --- uncertainty --- insurance --- coal --- spillover effects --- dynamic approaches --- forecasting --- logistic regression --- random forests --- support vector machines --- US natural gas crises --- XGboost --- neural networks --- oil futures prices crashes --- foresting --- logistical regression --- extreme gradient boosting --- moving window --- SVAR --- oil price --- gas price --- US macroeconomic aggregates --- GDP --- CPI