This study presents a conceptual framework for analyzing urban resilience in the context of climate change. The key conceptual elements of resilience are first identified and then reorganized with a focus on cities and climate change adaptation. This study covers not only ecological and engineering resilience but also resilience as a sociopolitical process from an evolutionary perspective. The study’s conceptual framework centers on resilience as it relates to cities and climate change. Its findings are expected to shed light on future urban planning and policies for adapting to climate change.

This study aims to present an integrated theoretical framework of sustainable local and regional development with an emphasis on social economy theory. The theoretical literature on social economy and sustainable local and regional development are reviewed and integrated focusing on the obstacles of sustainability and the function of social economy in local and regional development. Conflict and competition among sustainability values necessitates agreement and cooperation among those who embrace such values. Social enterprises are the core components of a social economy that reflects local and regional needs and pursues complex goals. In the social innovation process, social enterprises collaborate with various stakeholders regarding their business and social goals. Social enterprises have positive effects that influence local and regional development by satisfying local and regional needs, creating jobs in the community, developing relational assets in business processes, and restoring community solidarity. In social economy theory, social enterprises can facilitate sustainable local and regional development by including relational assets which embody social capital in social innovation processes. In the context of local and regional development, relational assets are important reinforcements of institutional capabilities, networks, and community or regional cooperation. Although social enterprises are not the dominant resources in traditional local and regional development, they can solve problems related to collective actions by networking and applying social norms to the situation.

The purpose of this study was to develop a low impact development-based district unit planning (LID-DP) model and to verify the model by applying it to a test site. To develop the model, we identified various barriers to the urban planning process and examined the advantages of various LID-related techniques to determine where in the urban development process LID would provide the greatest benefit. The resulting model provides (1) a set of district unit planning processes that consider LID standards and (2) a set of evaluation methods that measure the benefits of the LID-DP model over standard urban development practices. The developed LID-DP process is composed of status analysis, comprehensive analysis, basic plan, and sectoral plans. To determine whether the LID-DP model met the proposed LID targets, we applied the model to a test site in Cheongju City, Chungcheongbuk-do Province, Republic of Korea. The test simulation showed that the LID-DP plan reduced nonpoint source pollutants (total nitrogen, 113%; total phosphorous, 193%; and biological oxygen demand, 199%); reduced rainfall runoff (infiltration volume, 102%; surface runoff, 101%); and improved the conservation rate of the natural environment area (132%). The successful application of this model also lent support for the greater importance of non-structural techniques over structural techniques in urban planning when taking ecological factors into account.

This article aims to identify occupations at risk of climate change, and explore their spatial distribution by investigating their spatial concentration and cluster patterns in Korea. In order to identify professions vulnerable to climate change, we used three criteria: (1) exposure to the risks of job activities; (2) sensitivity to work environments; and (3) adaptive capacities based on the socioeconomic conditions of a person’s occupation. We identified 26 vocations as vulnerable, which corresponds to 16.5% of the total number of careers. Then, we used exploratory spatial data analysis (ESDA) techniques to examine the spatial distribution of jobs at risk of climate change effects. Endangered occupations show the extremely uneven spatial distributions across regions, indicating that there is a wide range of values for the proportion of endangered occupations across regions. The regions with high proportions of professions in jeopardy show clear spatial clustering based on the large geographic belt from the southwest to the northeast. Our results suggest that policymakers should consider the specific geographies of vulnerable occupations. The current work provides helpful hints on regional policy coordination to promote a virtuous circle of sustainable regional development.

The Intergovernmental Panel on Climate Change 2013 report claims that climate change from human-induced greenhouse gas emissions will cause increasing temperatures in many regions and various detrimental effects such as rising sea levels, ecosystem changes, droughts, and floods. This study proposes a method for assessing the climate risks resulting from climate change as well as a tool that companies can use to assess those risks. The method for assessing climate risk is proposed in accordance with the ISO 31000 risk management process. We then design a web-based tool to implement the climate change risk assessment process. The data the tool generates enable companies to identify and analyze their climate risks to reduce potentially negative future financial impacts. The data on potential damage costs indicate that climate change is no longer an environmental issue but rather an economic one for companies, and the results presented through the proposed assessment method can be used to establish countermeasures and sustainable planning at companies. The results of this research are significant in that they provide companies with the critical information needed to improve their planning and response to climate risk.

A question fundamental to sustainable economic growth is whether a poor region tends to grow faster than a rich one, such that the poor region catches up with the rich region in terms of the level of per capita income. In this article, we apply the spatial panel data approach to the analysis of regional income convergence across 177 economic areas in the contiguous US states over the period from 1969 to 2009. Using data on per capita incomes in the functionally defined economic areas, we find that the absolute value of the estimated coefficient of the initial per capita income decreases in the spatial and time-period fixed effects spatial lag model and increases in the spatial and time-period fixed effects spatial error model. This result implies that the growth rate in a specific economic area will be not only directly affected by an exogenous shock introduced into that economic area but also be impacted more by both the indirect effects of the first-order neighboring economic areas and the induced effects of the higher-order neighboring economic areas. This gives helpful hints on the issue of spatial interaction and regional policy coordination to start a virtuous circle of sustainable economic growth.