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At present, financial agglomeration tendency in domestic and foreign countries is increasingly evident. Therefore, from a comparative perspective, this paper aims to assess and predict the financial agglomeration degree in central five cities.

According to the diversity of evaluating indexes and the uncertainty of financial agglomeration, this paper constructs a set of indexes of evaluating the financial agglomeration degree, comprehensively evaluates the financial agglomeration degree of the five cities – Wuhan, Changsha, Zhengzhou, Nanchang and Hefei – in China's middle region from 2001 to 2010 by using the multiple dimension grey fuzzy decision-making model, and predicts their development tendency by using the GM (1, 1, β) model.

The results show that the multiple dimension grey fuzzy decision-making pattern cannot only be used to determine the weights of evaluating indexes, but also get the fuzzy partition and ranking order of the financial agglomeration in central five cities. The grey prediction results can objectively reflect the development tendency of the financial agglomeration in central five cities.

From the results, it is necessary for any competitive city to clarify their relative strengths and weaknesses in order for the accurate location and scientific development, and it also provides a reference for the government decision-making.

The paper succeeds in using the multiple dimension grey fuzzy decision-making model to measure the financial agglomeration degree of the five central cities and the grey prediction model to predict future trends.

The level of financial development is a key factor influencing long-term economic growth. A high level of financial development allows for the effective diversification of risk and allocation of capital, which, in the long run, improves the growth prospects of an economy. Schumpeter (1911) was one of the first to highlight the importance of financial development as a determinant of economic growth. Recent empirical work supports this relationship (see Beck & Levine, 2002; Levine, 2004; Mishkin, 2007). For example, Levine (2004) summarizes the empirical evidence on financial development and economic growth and states that “the level of financial development is a good predictor of future rates of economic growth, capital accumulation and technological change” (Levine, 1997, p. 689).² Thus, stock and forward markets spread knowledge about market expectations of factors and changes that are important for economic development (Lachmann, 1978).

This study employs firm-level data to evaluate how the knowledge economy impacts the financing constraints of businesses across 106 low- and middle-income nations, focusing on the influence of technological transformation on corporate financing choices.

The research centers on privately held, unlisted firms and examines the distinct effects of knowledge at both the within-country and between-country levels using a panel dataset. Rigorous sensitivity and endogeneity analyses are conducted to ensure the reliability of the findings.

The findings indicate that greater levels of the knowledge economy correlate with reduced financing constraints for firms. However, this effect varies depending on the location within a country and across different geographical regions. Firms situated in larger urban centers and more innovative regions reap the most significant benefits from the knowledge economy when seeking external funding. Conversely, firms in smaller cities, rural areas and regions characterized by structural and institutional inefficiencies in knowledge generation experience fewer advantages.

The impact of knowledge exhibits variability not only within and among countries but also between poor and affluent developing nations, as well as between larger and smaller countries. The knowledge effect on firms' access to external finance is influenced by factors such as financial openness and development, educational quality, technological absorption capabilities and agglomeration conditions within each country.

This study aims to determine how much company participation in a type of cluster affects its economic performance.

This study includes companies operating in seven industries (automotive, engineering, textiles, information technology (IT) services, furniture, packaging and nanotechnology) in the Czech Republic. The companies are divided into three groups: members of institutionalized cluster, operating in the same region (natural clusters) and operating in other regions. Data envelopment window analysis is used to measure their performance for 2009–2019.

Results show that the effect of clustering differs among industries. Companies in three industries (automotive, engineering, nanotechnology) reveal a positive impact of the cluster initiative on performance growth. Two industries (textile, packaging) with companies operating in a natural cluster show better performance than those in an institutionalized cluster. Moreover, the IT services and the furniture industries show no positive effect of clustering on corporate performance.

This research includes 686 companies from seven industries and monitored for 11 years. On the one hand, the sample includes a relatively high number of companies overall; but on the other hand, the sample is relatively small, especially for nonclustered companies. The reason is the lack of available financial statements for small companies.

From the perspective of practical cluster policy, the authors can recommend that monitoring the performance of member companies in clusters must be one of the criteria for evaluating the success of a cluster, such as cluster initiatives.

This study distinguishes between long-standing natural clusters in a given industry and institutionalized ones that have emerged because of a top-down initiative. An original database is created for clustered and nonclustered companies in seven industries, covering the entire Czech Republic.

This paper aims to explore the impact of digital inclusive finance (DIF) on regional economic growth and innovation-driven development.

Based on the panel data of 31 provinces (autonomous regions and municipalities directly under the central government) in China from 2011 to 2018, this paper explores the impact of DIF on economic growth and innovative development.

(1) DIF has a direct positive effect on economic growth and innovative development; (2) there is significant regional heterogeneity in the impact of DIF on economic growth and innovative development. (3) DIF can indirectly affect economic growth and innovative development by increasing residents’ personal disposable income, increasing fiscal expenditure and improving educational level.

Exploring the relationship between them and digital inclusive financial development can provide a reference for national productivity construction and development.

Economic growth and innovation-driven development have been one of the main concerns of China’s policymakers. Exploring the relationship between them, digital inclusive financial development can provide a reference for national productivity construction and development.

Addresses issues and questions in an evaluative review of the insolvency of office developer Olympia and York at Canary Wharf in 1992. Asks: what made a large, private company internationally vulnerable? Why could property cycles with their risks become internationalized, notwithstanding that sometimes spreading the internationalization of property could be considered as a useful means of pooling the risks? What was the nature of the office building cycle in London which was adapting its property needs to the internationalization of the financial sector in London, Tokyo and New York? What lessons emerge in the relationships between economics, finance and law in a new wave of insolvency proceedings under post‐1980 legislation? In terms of property market conditions, how can Paul Reichmann’s consortium’s repurchase of Canary Wharf be explained?

One aspect of agglomeration economies is economies of scale. When automobile production centered in Detroit in the early part of the twentieth century, this allowed more efficient production methods, which lowered the per-unit cost of output. Arrow (2000) emphasizes the tension between increasing returns to scale and equilibrium models, and as Young (1928) noted, increasing returns to scale is at the foundation of economic progress. Kaldor (1972), building on Young's insights, noted that static neoclassical economic models did not do a good job of depicting the economic progress that results from increasing returns to scale in production. This insight goes at least as far back as Adam Smith (1776), however, who noted the increased productivity that comes with an increased division of labor. Smith's example of the pin factory, where individuals specializing in one small part of a larger manufacturing operation increase productivity by, perhaps, hundreds of times, shows the benefits of agglomeration economies. The division of labor is limited by the extent of the market, Smith argued, so enlarging the extent of the market allows for a greater division of labor, which increases productivity and generates prosperity. By concentrating automobile production in Detroit rather than having automobiles locally built, the extent of the market is increased from one locality to an entire nation, and in some cases an entire world. The resulting agglomeration economies increase productivity and produce prosperity.

Agricultural carbon productivity considers the dual goals of “agricultural economic growth” and “carbon emission reduction”. Improving agricultural carbon productivity is a requirement for promoting green and low-carbon development of agriculture. Agricultural production agglomeration is widespread worldwide, but the relationship between agricultural production agglomeration and agricultural carbon productivity is inconclusive. This paper aims to study the impact of agricultural production agglomeration on agricultural carbon productivity, which is conducive to a better understanding of the relationships among agglomeration, agricultural economic development and carbon emission, better planning of agricultural layout to build a modern agricultural industrial system and achieve the goal of carbon peaking and carbon neutrality.

Based on China's provincial data from 1991 to 2019, this paper uses non-radial directional distance function (NDDF) and Metafrontier Malmquist–Luenberger (MML) productivity index to measure total factor agricultural carbon productivity. Subsequently, using a panel two-way fixed effect model to study the effect and mechanism of agricultural production agglomeration on agricultural carbon productivity, and the two-stage least squares method (IV-2SLS) is used to solve endogeneity. Finally, this paper formulates a moderating effect model from the perspective of the efficiency of agricultural material capital inputs.

The empirical results identify that Chinese provincial agricultural carbon productivity has an overall growth trend and agricultural technological progress is the major source of growth. There is an inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity. The input efficiency of agricultural film, machine and water resources have moderating effects on the inverted U-shaped relationship. Agricultural production agglomeration also promotes agricultural carbon productivity by inhibiting agricultural carbon emissions in addition to affecting agricultural input factors and its internal mechanisms are agricultural green technology progress and rural human capital improvement.

This paper innovatively adopts the NDDF–MML method to measure the total factor agricultural carbon productivity more scientifically and accurately and solves the problems of ignoring group heterogeneity and the shortcomings of traditional productivity measurement in previous studies. This paper also explains the inverted U-shaped relationship between agricultural production agglomeration and agricultural carbon productivity theoretically and empirically. Furthermore, from the perspective of agricultural material capital input efficiency, this paper discusses the moderating effect of input efficiency of fertilizers, pesticides, agricultural film, agricultural machines and water resources on agricultural production agglomeration affecting agricultural carbon productivity and answers the mechanism of carbon emission reduction of agricultural production agglomeration.

The purpose of this paper is to investigate the relationship between office rents and mixed-use development in the context of agglomeration economies.

Using a sample of 10,209 observations in 100 Grade A office buildings in Hong Kong from January 2001 to June 2011, the authors estimated office rent regression using unbalanced panel data analysis.

The results show that rents decreased with an increase in distance from retailers and hotels. Furthermore, the results revealed that, ceteris paribus, office tenants were willing to pay higher rents in a mixed-use than in a single-use office development.

There is an existence of agglomeration economies due to the clustering of various industries in mixed-use developments, which allow for their close proximity to potential clients.

The diversity of activities in a mixed-use development benefit its tenants and, thus, convince them to pay higher rents. Higher rents generated by a mixed-use facility will attract more investors to it. Investors should seek opportunities to capitalize on their equity in mixed-use developments.

This paper attempts to uncover a relationship between office rents and mixed-use developments by drawing on the concept of agglomeration economies.

The purpose of this article is to analyze the supply (technology, education, labour, unemployment and real estate development) and demand (fiscal revenue and resident income) factors that influence regional entrepreneurial activity in China. Entrepreneurship develops at a rapid pace in China with significant differences among the country’s regions.

Statistics of 31 Chinese provinces from 2005 to 2010 were collected, and an econometric model of the panel data was established.

Empirical results show that technology and employment positively impact on regional entrepreneurial activity. A subsequent analysis comparing data from 2005-2008 to 2009-2010 showed that different variables on regional entrepreneurship weaken during a period of financial crisis, with technology remaining as the only significant variable across all models. Finally, the study summarizes China’s entrepreneurial activity as primarily supply-driven.

This study is limited by the data sources and index design, which may not fully capture all influences on regional entrepreneurship to determine whether an inflection point or other interaction mechanisms exist.

The study demonstrates a differential emphasis on the impact of economic supply factors in a developing economy to positively affect entrepreneurial activities and sustained economic growth at the regional level. Conversely, it can be inferred that increased government spending during an economic crisis positively influences regional entrepreneurial activities.

The study contributes toward the development of a theoretical framework that emphasizes the relationship between entrepreneurial activities and its regional supply and demand factors. The overall model and findings highlight technology’s importance on the development of innovation clusters that spur industrial agglomeration.