Showing papers on "Spillover effect published in 2019"

Meta-analysis of pro-environmental behaviour spillover

Abstract: When people engage in a first pro-environmental behaviour (PEB1; for example, conserving energy at home), are they more or less likely (positive and negative spillover, respectively) to engage in other pro-environmental behaviours (‘PEB2’; for example, conserving water at home)? We examined evidence for spillover using a meta-analysis of interventions. We coded 22 studies and unpublished data that fulfilled the following criteria: used experimental or quasi-experimental design, showed change in a PEB1 and measured at least one PEB2. Analysis of the 77 effect sizes found in these studies showed that the overall spillover from a PEB1 was positive, though small, on the intention to perform a PEB2 (pooled mean effect size estimate d+ = 0.17). However, the spillover effect was negative and small for actual behaviour (d+ = −0.03) and policy support (d+ = −0.01) for PEB2. Positive spillover was most likely when interventions targeted intrinsic motivation and when PEB1 and PEB2 were similar. Future research in the area should target and measure spillover processes, collect larger samples and statistically test for spillover in more consistent ways. An environmentally friendly behaviour is more likely to motivate a second such behaviour when both actions are similar and when the first behaviour is intrinsically motivated, according to a review of the literature.

Community attachment, tourism impacts, quality of life and residents’ support for sustainable tourism development

Abstract: This study examined the formation of residents’ support for sustainable tourism development based on the social exchange theory and bottom-up spillover theory. A self-administered survey along with...

Clean air for some: Unintended spillover effects of regional air pollution policies

Abstract: China has enacted a number of ambitious pollution control policies to mitigate air pollution in urban areas. Unintended side effects of these policies to other environmental policy arenas and regions have largely been ignored. To bridge this gap, we use a multiregional input-output model in combination with an atmospheric chemical transport model to simulate clean air policy scenarios and evaluate their environmental impacts on primary PM2.5 and secondary precursor emissions, as well as CO2 emissions and water consumption, in the target region and spillover effects to other regions. Our results show that the reduction in primary PM2.5 and secondary precursor emissions in the target regions comes at the cost of increasing emissions especially in neighboring provinces. Similarly, co-benefits of lower CO2 emissions and reduced water consumption in the target region are achieved at the expense of higher impacts elsewhere, through outsourcing production to less developed regions in China.

U.S. Monetary Policy and International Risk Spillovers

Abstract: I show that monetary policy divergence vis-a-vis the U.S. has larger spillover effects in emerging markets than advanced economies. The monetary policy of the U.S. affects domestic credit costs in other countries through its effect on global investors’ risk perceptions. Capital flows in and out of emerging market economies are particularly sensitive to fluctuations in such risk perceptions and have a direct effect on local credit spreads. Domestic monetary policy is ineffective in mitigating this effect as the pass-through of policy rate changes into short-term interest rates is imperfect. This disconnect between short rates and monetary policy rates is explained by changes in risk perceptions. A key policy implication of my findings is that emerging markets’ monetary policy actions designed to limit exchange rate volatility can be counterproductive. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Modeling and Forecasting Regional Tourism Demand Using the Bayesian Global Vector Autoregressive (BGVAR) Model

Abstract: Increasing levels of global and regional integration have led to tourist flows between countries becoming closely linked. These links should be considered when modeling and forecasting international tourism demand within a region. This study introduces a comprehensive and accurate systematic approach to tourism demand analysis, based on a Bayesian global vector autoregressive (BGVAR) model. An empirical study of international tourist flows in nine countries in Southeast Asia demonstrates the ability of the BGVAR model to capture the spillover effects of international tourism demand in this region. The study provides clear evidence that the BGVAR model consistently outperforms three other alternative VAR model versions throughout one- to four-quarters-ahead forecasting horizons. The potential of the BGVAR model in future applications is demonstrated by its superiority in both modeling and forecasting tourism demand.

Ecological interventions to prevent and manage zoonotic pathogen spillover

Abstract: Spillover of a pathogen from a wildlife reservoir into a human or livestock host requires the pathogen to overcome a hierarchical series of barriers. Interventions aimed at one or more of these barriers may be able to prevent the occurrence of spillover. Here, we demonstrate how interventions that target the ecological context in which spillover occurs (i.e. ecological interventions) can complement conventional approaches like vaccination, treatment, disinfection and chemical control. Accelerating spillover owing to environmental change requires effective, affordable, durable and scalable solutions that fully harness the complex processes involved in cross-species pathogen spillover. This article is part of the theme issue 'Dynamic and integrative approaches to understanding pathogen spillover'.

Spatial spillover effect and driving forces of carbon emission intensity at the city level in China

Abstract: In this study, we adopt kernel density estimation, spatial autocorrelation, spatial Markov chain, and panel quantile regression methods to analyze spatial spillover effects and driving factors of carbon emission intensity in 283 Chinese cities from 1992 to 2013. The following results were obtained. (1) Nuclear density estimation shows that the overall average carbon intensity of cities in China has decreased, with differences gradually narrowing. (2) The spatial autocorrelation Moran’s I index indicates significant spatial agglomeration of carbon emission intensity is gradually increasing; however, differences between regions have remained stable. (3) Spatial Markov chain analysis shows a Matthew effect in China’s urban carbon emission intensity. In addition, low-intensity and high-intensity cities characteristically maintain their initial state during the transition period. Furthermore, there is a clear “Spatial Spillover” effect in urban carbon emission intensity and there is heterogeneity in the spillover effect in different regional contexts; that is, if a city is near a city with low carbon emission intensity, the carbon emission intensity of the first city has a higher probability of upward transfer, and vice versa. (4) Panel quantile results indicate that in cities with low carbon emission intensity, economic growth, technological progress, and appropriate population density play an important role in reducing emissions. In addition, foreign investment intensity and traffic emissions are the main factors that increase carbon emission intensity. In cities with high carbon intensity, population density is an important emission reduction factor, and technological progress has no significant effect. In contrast, industrial emissions, extensive capital investment, and urban land expansion are the main factors driving the increase in carbon intensity.