Abstract
Climate change entails potential risks for investors, and its effects on investment has spread beyond physical borders. This study investigates how multinational corporations (MNCs) incorporate climate risks into their decisions regarding foreign direct investments (FDIs). We find that large differences in the climate risks of home and host cities discourages FDI by increasing cross-border adaptation costs. Such impacts are particularly pronounced among environmentally sensitive industries that are more exposed to climate risks. Further analysis reveals that city-based smartness factors mitigate the negative impacts of climate risk differences on FDI by reducing adaptation costs and engendering new business opportunities. This study provides new evidence on the profound effects of climate risks on FDI and how smart cities can increase their resilience to climate risks in the context of international business.
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Data availability
Greenfield FDIs data are from Financial Times Ltd.’s fDi Markets, which is a commercial data source and is not publicly available. Climate-related information is taken from the Terrestrial Air Temperature and Precipitation data of the National Oceanic and Atmospheric Administration.
Notes
Supplementary material. Dell et al. (2012) estimate the interaction between climate change and economic growth. Dell et al. (2014) also assess the potential economic effects of future climate change on the particualr channels of labor productivity, political stability, energy use, health, and migration. Burke et al. (2016) and van Vuuren et al. (2020) find that refining a climate policy can delay and mitigate impact of uncertainties and damages on econonic development.
Initially, smart cities were prompted in the developed countries where Japan and EU are the two most representative projects. In Japan, it seeks to make its city more environmental soundly and resilient especially after the Great East Japan Earthquake (Yamagata & Seya 2013). In EU, the primary target was to reduce greenhouse gas emissions. The initiatives centers on smart economy, smart mobility, smart environment, smart people, smart living, and smart governance (EU 2014).
There are 62,981 (78%) city pairs that have only one FDI project record during our sample period and 78 city pairs with zero investment value for their corresponding project.
Source: https://easyparkgroup.com/
The coefficient of climate risk difference on FDI between home and host city in column 1 is − 0.013. Thus, the changes in investment for a one-standard-deviation (\(4.270\)) decrease in climate risk difference equals 49.336 \(*4.270*{(e}^{-0.013}-1)=-2.721.\)
Full details of environmentally sensitive industry are not included in this article in order to optimize space. The specific environmentally sensitive sector can be retrieved from: OSF | Supplementary Material for the List of Environmentally Sensitive Industry.
We construct a dummy proxy to identify the primary city if home or host city is a prominent one (We define the type of city following the website: https://simplemaps.com/data/world-cities). We further expand the baseline model to include the interaction between climate risk differences and primary city dummy (difference primary city). The results are exhibited in Appendix Table 8. The results imply that the effect of climate risk on FDI decisions matters for the size of home or host city to some extent. In column 1, the coefficient of the interaction term is positive and significant which indicates the negative effect of climate risk differences on FDI is weaker when outward direct investments flow to primary host city. To address the heterogenous resilience effect, we interact the dummy for environmentally sensitive industry with difference smart. The results are demonstrated from column 1 to 5 in Appendix Table 9 and generally indicate that the resilient effect of smart cities on the climate-FDI is stronger in environmentally sensitive industry. Additionally, we construct a dummy variable equal to one if a city pair where the host city is listed within top 100. We expand the resilient effects model to include a triple interaction between climate risk difference, environmentally sensitive industry dummy and smartness city dummy within top-100 list (difference smart within top 100 industry). The robustness result is shown in column 6 of Appendix Table 9. The triple interaction term implies that a city pair in which the host city is within top-100 smartness list while home city is not, the resilient effect of smart cities on climate-FDI relation is stronger in environmentally sensitive industry. The interaction term for Difference Smart shows that the magnitude of resilient effect for smartness within top-100 cities is larger and significant than that of between top 100 and non-top-100.
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Acknowledgements
We are grateful for the insightful comments from the editor and three reviewers.
Funding
This work was supported by National Natural Science Foundation of China (Project No. 72140005), Research Grants Council of the Hong Kong Special Administrative Region (Project No. CityU 21610019), Singapore MOE grant, and CORE project grant. CORE is a joint research centre for ocean research between QNLM and HKUST.
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All authors contributed to the study conception and design. Data collection and analysis were performed by Y.A. and L.Z. The first draft of the manuscript was jointly written by all authors. All authors read and approved the final manuscript.
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An, Y., Liu, N., Zhang, L. et al. Adapting to climate risks through cross-border investments: industrial vulnerability and smart city resilience. Climatic Change 174, 10 (2022). https://doi.org/10.1007/s10584-022-03431-x
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DOI: https://doi.org/10.1007/s10584-022-03431-x