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AbstractUniversity–industry interactions have been extensively discussed by economic geographers. However, while universities increasingly engage in local and nonlocal interactions with the industry, there is a lack of evidence concerning the consequences of universities becoming involved in these open innovation processes. Therefore, this study empirically examines the effects of local and nonlocal knowledge interactions on the innovative performance of universities by utilizing a large panel database constructed from a sample of 185 universities in China. First, we observe that both local and nonlocal interactions have an inverted U‐shaped relationship with university innovative performance. Second, local interactions combined with nonlocal interactions have a negative effect on the innovative performance of universities. Finally, we find that a balance is required between local and nonlocal interactions to enhance university performance. These results have important implications for policy design.

AbstractThe discussion on innovation networks of patents or papers has attracted many economic geographers; however, the Industrial Technology Innovation Strategy Alliance (ITISA) has been ignored, although it is also an important innovation form of firms, and the formation mechanism of alliance innovation networks is unclear. This study is based on the data of Shanghai high‐tech ITISAs between 2010 and 2015, and employs the methods of social networks and negative binomial regression to analyse the actor structure, spatial structure, and proximity mechanism of innovation networks of Shanghai high‐tech ITISAs. Results highlight the following: (a) Firms comprise the largest number of actors in alliance innovation networks, and universities, research institutions, and industry associations also play some roles. (b) The local and rooted characteristics of the innovation networks of ITISAs are obvious. The spatial distribution of innovation partners is mainly in Shanghai, and a few innovation actors are located in neighbouring cities, such as Suzhou, Hangzhou, and Ningbo in the Yangtze River Delta Urban Agglomeration. (c) Proximity significantly promotes the formation of innovation networks in an ITISA and contributes to the improvement of innovation ability, and organizational proximity plays a greater role than geographical proximity or cognitive proximity.

AbstractReducing carbon emissions is the only way to solve the global climate problem, which obviously requires the joint efforts of all regions. From the perspective of carbon emission reduction demand, this research obtains the data of low‐carbon patents and CO2 emissions from 2000 to 2019, and then explores the innovation effect of low‐carbon technology transfer in the Yangtze River Economic Belt (YREB). We draw the following conclusions: (1) Low‐carbon technology transfer has a positive impact on low‐carbon technology innovation. However, a large number of low‐carbon technology transfers are concentrated between developed regions in the YREB at present. (2) No matter from the perspective of total amount or efficiency, carbon emission reduction demand can play positive moderating role in the process of exerting the innovation effect of low‐carbon technology transfer, indicating that the transfer of low‐carbon technologies to regions with high demand is efficient. (3) The moderating effect of carbon emission reduction demand in different regions has regional heterogeneity. Carbon emissions per unit of GDP plays a greater role in moderating the innovation effect of low‐carbon technology transfer in the eastern region, while CO2 emissions plays a greater role in the central and western regions. Therefore, we propose to strengthen the transfer of low‐carbon technologies between developed and less‐developed regions, and adopt differentiation strategies according to the actual situation of each region. Our research has deepened the understanding of environmental economic geography on the innovation effect of low‐carbon technology transfer, and has reference significance for global low‐carbon technology cooperation.

This article investigates the drivers of investment growth in emerging market and developing economies with a focus on the most recent slowdown over the 2010–2015 period. Using panel regression techniques, we find that the recent investment slowdown in emerging market and developing economies is associated with a range of obstacles: weak economic activity, negative terms-of-trade shocks, declining foreign direct investment inflows, elevated private debt burdens, and heightened political risk. This stands in contrast with advanced economies, where weak economic activity is the most important factor. We briefly discuss policy implications of our findings.

Investment growth in emerging market and developing economies has slowed sharply since 2010. This paper presents a comprehensive analysis of the causes and implications of this slowdown and presents a menu of policy responses to improve investment growth. It reports four main results. First, the slowdown has been broad-based and most pronounced in the largest emerging markets and in commodity exporters. Second, it reflects a range of obstacles: weak activity, negative terms-of-trade shocks, declining foreign direct investment inflows, elevated private debt burdens, heightened political risk, and adverse spillovers from major economies. Third, by slowing capital accumulation and technological progress embedded in investment, weak post-crisis investment growth has contributed to sluggish growth of potential output in recent years. Finally, although specific policy priorities depend on country circumstances, policymakers can boost investment both directly, through public investment, and indirectly, by encouraging private investment, including foreign direct investment, and by undertaking measures to improve overall growth prospects and the business climate.

Investment growth in emerging market and developing economies has slowed sharply since 2010. This paper presents a comprehensive analysis of the causes and implications of this slowdown and presents a menu of policy responses to improve investment growth. It reports four main results. First, the slowdown has been broad-based and most pronounced in the largest emerging markets and in commodity exporters. Second, it reflects a range of obstacles: weak activity, negative terms-of-trade shocks, declining foreign direct investment inflows, elevated private debt burdens, heightened political risk, and adverse spillovers from major economies. Third, by slowing capital accumulation and technological progress embedded in investment, weak post-crisis investment growth has contributed to sluggish growth of potential output in recent years. Finally, although specific policy priorities depend on country circumstances, policymakers can boost investment both directly, through public investment, and indirectly, by encouraging private investment, including foreign direct investment, and by undertaking measures to improve overall growth prospects and the business climate.

A synchronous growth slowdown has been underway in emerging markets (EM) since 2010. Growth in these countries is now markedly slower than, not just the pre‐crisis average, but also the long‐term average. As a group, EM growth eased from 7.6 percent in 2010 to 4.5 percent in 2014, and is projected to slow further to below 4 percent in 2015. This moderation has affected all regions (except South Asia) and is the most severe in Latin America and the Caribbean. The deceleration is highly synchronous across countries, especially among large EM. By 2015, China, Russia, and South Africa had all experienced three consecutive years of slower growth. The EM‐AE growth differential has narrowed to two percentage points in 2015, well below the 2003‐08 average of 4.8 percentage points and near the long‐term average differential of 1990‐2008. The recent slowdown in EM has been a source of a lively debate, as evident from the quotations at the beginning of this note. Some economists paint a bleak picture for the future of EM and argue that the impressive growth performance of EM prior to the crisis was driven by temporary commodity booms and rapid debt accumulation, and will not be sustained. Others emphasize that a wide range of cyclical and structural factors are driving the slowdown: weakening macroeconomic fundamentals after the crisis; prospective tightening in financial conditions; resurfacing of deep‐rooted governance problems in EM; and difficulty adjusting to disruptive technological changes. Still others highlight differences across EM and claim that some of them are in a better position to weather the slowdown and will likely register strong growth in the future. This policy research note seeks to help move the debate forward by examining the main features, drivers, and implications of the recent EM slowdown and provides a comprehensive analysis of available policy options to counteract it.