This page has only limited features, please log in for full access.

Dr. Haichao Yu
Wuhan University

Basic Info


Research Keywords & Expertise

0 Applied Economics
0 Innovation
0 developed economies
0 regional economic
0 environment and energy policy

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
Profile ImageHaichao Yu Institute of Regional and Ur...
Following: 1 user
View all

Feed

Research article
Published: 15 June 2021 in Technology Analysis & Strategic Management
Reads 0
Downloads 0

The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is one of the most active innovation areas in China, but the complex institutional and cultural environment makes innovation cooperation among the cities challenging. Based on 2001–2019 data, this study analyses the spatial pattern and externalities of GBA’s knowledge innovation network using a social network method and spatial econometric model. Results show that Guangzhou and Hong Kong have always been the cores of knowledge innovation in network. Shenzhen emerged as an innovation centre after 2012, and other cities have become peripheral areas in the network. Small- and medium-sized cities do not benefit from the innovative development of core cities but are trapped in their agglomeration shadow. Institutional and cultural differences are the main obstacles hindering innovation cooperation between cities. In comparison, distance has fewer limitations on innovation cooperation. The negative externality of knowledge innovation network indicates that this region should narrow the gap of spatial differences, optimise the innovation network pattern to improve the network externalities.

ACS Style

Wenyi Yang; Fei Fan; Xueli Wang; Haichao Yu. Knowledge innovation network externalities in the Guangdong–Hong Kong–Macao Greater Bay Area: borrowing size or agglomeration shadow? Technology Analysis & Strategic Management 2021, 1 -18.

AMA Style

Wenyi Yang, Fei Fan, Xueli Wang, Haichao Yu. Knowledge innovation network externalities in the Guangdong–Hong Kong–Macao Greater Bay Area: borrowing size or agglomeration shadow? Technology Analysis & Strategic Management. 2021; ():1-18.

Chicago/Turabian Style

Wenyi Yang; Fei Fan; Xueli Wang; Haichao Yu. 2021. "Knowledge innovation network externalities in the Guangdong–Hong Kong–Macao Greater Bay Area: borrowing size or agglomeration shadow?" Technology Analysis & Strategic Management , no. : 1-18.

Article
Published: 07 June 2021 in Environment, Development and Sustainability
Reads 0
Downloads 0

The slack-based measure (SBM) model was used in this study to calculate the urban green innovation efficiency (GIE) of Chinese 283 cities during 2008–2018, and the night light data from the defense meteorological satellite program/operational linescan system (DMSP/OLS) were used to characterize the economic development level. On the basis, efforts were made to analyze how ecological footprint is affected by urban GIE at varying economic development levels under the Hansen threshold regression model and reveal the mechanism for ecological footprint to receive influence from urban GIE through the mediation effect model. The results show that: (1) The improvement in the urban GIE of the investigated cities during the study period has a negative double threshold in influencing ecological footprint throughout China. However, with higher economic development level, the inhibitory effect gradually weakens, with the elastic coefficient changing from − 0.3046 and − 0.2132 to − 0.1392 at a 1% significant level. (2) The inhibitory effect on ecological footprint from urban GIE is spatially heterogeneous in Chinese cities. In eastern cities other than central and western cities, urban GIE exerts the strongest inhibitory effect on ecological footprint, with the corresponding coefficient being − 0.3972 at a 1% significant level. Moreover, the inhibition in eastern and central regions is strengthened with higher economic development level. Nevertheless, before crossing the second threshold, the inhibitory effect of urban GIE on ecological footprint in western China does not appear, with the coefficient being 0.1899 and 0.1379, respectively, with at a 1% significant level. (3) Industrial structure and energy structure play a mediating role in the effect of urban GIE on ecological footprint. By contrast, population aggregation and infrastructure are important driving forces for the increase of ecological footprint.

ACS Style

Haiqian Ke; Shangze Dai; Haichao Yu. Effect of green innovation efficiency on ecological footprint in 283 Chinese Cities from 2008 to 2018. Environment, Development and Sustainability 2021, 1 -20.

AMA Style

Haiqian Ke, Shangze Dai, Haichao Yu. Effect of green innovation efficiency on ecological footprint in 283 Chinese Cities from 2008 to 2018. Environment, Development and Sustainability. 2021; ():1-20.

Chicago/Turabian Style

Haiqian Ke; Shangze Dai; Haichao Yu. 2021. "Effect of green innovation efficiency on ecological footprint in 283 Chinese Cities from 2008 to 2018." Environment, Development and Sustainability , no. : 1-20.

Journal article
Published: 06 April 2021 in Environmental Technology & Innovation
Reads 0
Downloads 0

Improving the level of innovation is a driving force for the sustainable development of urban economy, ecology and society. Improved data envelopment analysis (DEA) is used herein to investigate 280 cities in China and measure their innovation efficiency during 2012–2018, and a spatial measurement model is applied to analyse the impact of innovation efficiency on ecological footprint. In addition, this paper further discusses the impact of innovation efficiency on ecological footprint of different regions, comparing innovative and non-innovative cities, and eastern, central and western cities. The major findings are as follows: (1) A significant positive spatial relationship exists in the ecological footprint of Chinese cities as a whole, including eastern and central cities. (2) Promoting innovation efficiency significantly inhibits the ecological footprint of not only local region, but also neighbouring regions, with coefficients of −0.2488 and −0.1638 respectively. (3) The inhibitory effect of the increase in innovation efficiency in innovative cities on ecological footprint is stronger in local and neighbouring regions than non-innovative cities. In eastern and central Chinese cities, local ecological footprint is subject to a potent inhibitory effect from the progress in innovation efficiency, while the improvement in innovation efficiency has shown a significant promoting effect in the western region; and the ecological footprint of neighbouring regions only in the eastern cities is inhibited by the improvement of innovation efficiency of local regions.

ACS Style

Haiqian Ke; Shangze Dai; Haichao Yu. Spatial effect of innovation efficiency on ecological footprint: City-level empirical evidence from China. Environmental Technology & Innovation 2021, 22, 101536 .

AMA Style

Haiqian Ke, Shangze Dai, Haichao Yu. Spatial effect of innovation efficiency on ecological footprint: City-level empirical evidence from China. Environmental Technology & Innovation. 2021; 22 ():101536.

Chicago/Turabian Style

Haiqian Ke; Shangze Dai; Haichao Yu. 2021. "Spatial effect of innovation efficiency on ecological footprint: City-level empirical evidence from China." Environmental Technology & Innovation 22, no. : 101536.

Journal article
Published: 07 February 2021 in International Journal of Environmental Research and Public Health
Reads 0
Downloads 0

We analyze the mechanism for industrial co-agglomeration in Chinese 283 cities to affect haze pollution from 2003 to 2016 and examine the possible mediating effects of urbanization and energy structure between haze pollution and industrial co-agglomeration, finally obtaining the following results. First, industrial co-agglomeration and haze pollution across China, including central and eastern regions keep a typical inverted U-shaped curve relationship. That is, industrial co-agglomeration first promotes haze pollution and then restrains it. However, the impact of industrial co-agglomeration on haze pollution in western China is still on the left side of the inverted U-shaped curve, reflecting a promotion effect. Second, industrial co-agglomeration has a significant spatial spillover effect on haze pollution. Additionally, industrial co-agglomeration can promote haze pollution in local regions but inhibit it in surrounding regions in both the short and long run. In contrast, when the industrial co-agglomeration index exceeds the inflection point (3.6531), it benefits the reduction of haze pollution in local regions, while not being conducive to it in the neighboring regions. Third, industrial co-agglomeration can affect haze pollution through urbanization and energy structure, that is, urbanization and energy structure play an intermediary role between them.

ACS Style

Yunling Ye; Sheng Ye; Haichao Yu. Can Industrial Collaborative Agglomeration Reduce Haze Pollution? City-Level Empirical Evidence from China. International Journal of Environmental Research and Public Health 2021, 18, 1566 .

AMA Style

Yunling Ye, Sheng Ye, Haichao Yu. Can Industrial Collaborative Agglomeration Reduce Haze Pollution? City-Level Empirical Evidence from China. International Journal of Environmental Research and Public Health. 2021; 18 (4):1566.

Chicago/Turabian Style

Yunling Ye; Sheng Ye; Haichao Yu. 2021. "Can Industrial Collaborative Agglomeration Reduce Haze Pollution? City-Level Empirical Evidence from China." International Journal of Environmental Research and Public Health 18, no. 4: 1566.

Journal article
Published: 06 January 2021 in International Journal of Environmental Research and Public Health
Reads 0
Downloads 0

Innovation agglomeration plays a decisive role in improving the input–output scale and marginal output efficiency of factors. This paper takes carbon emissions as the unexpected output and energy consumption as the input factor into the traditional output density model. The dynamic spatial panel Durbin model is used to analyze the mechanism for innovation agglomeration and energy intensity to affect carbon emissions from 2004 to 2017 in thirty Chinese provinces. Then, we test the possible mediating effect of energy intensity between innovation agglomeration and carbon emissions. The major findings are as follows. (1) The carbon emission intensity has time-dependence and positive spatial spillover effect. That is, there is a close correlation between current and early carbon emissions, and there is also a high-degree correlation between regional and surrounding areas’ carbon emissions. (2) Carbon emissions keep a classical inverted U-shaped relation with innovation agglomeration, as well as with energy intensity. However, the impact of innovation agglomeration on carbon emissions in inland regions of China does not appear on the right side of the inverted U-shaped curve, while carbon emissions are subject to a positive nonlinear promoting effect from energy intensity. (3) When the logarithm of innovation agglomeration is more than 3.0309, it first shows the inhibition effect on energy intensity. With the logarithm of innovation agglomeration exceeding 5.0100, it will show the dual effect of emission reduction and energy conservation. (4) Energy intensity could work as the intermediary variable of innovation agglomeration’s influence on carbon emissions. Through its various positive externalities, innovation agglomeration can produce a direct impact on carbon emissions, and through energy intensity, it can also affect carbon emissions indirectly.

ACS Style

Jianqing Zhang; Haichao Yu; Keke Zhang; Liang Zhao; Fei Fan. Can Innovation Agglomeration Reduce Carbon Emissions? Evidence from China. International Journal of Environmental Research and Public Health 2021, 18, 382 .

AMA Style

Jianqing Zhang, Haichao Yu, Keke Zhang, Liang Zhao, Fei Fan. Can Innovation Agglomeration Reduce Carbon Emissions? Evidence from China. International Journal of Environmental Research and Public Health. 2021; 18 (2):382.

Chicago/Turabian Style

Jianqing Zhang; Haichao Yu; Keke Zhang; Liang Zhao; Fei Fan. 2021. "Can Innovation Agglomeration Reduce Carbon Emissions? Evidence from China." International Journal of Environmental Research and Public Health 18, no. 2: 382.

Journal article
Published: 13 December 2018 in Sustainability
Reads 0
Downloads 0

Economic resilience is a critical indicator of the sustainable development of an urban economy. This paper measures the urban economic resilience (UER) of 286 major cities in China from six indicators—economic growth, opening up, social development, environmental protection, natural conditions, and technological innovation—using a subjective and objective weighting method and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. Furthermore, kernel density estimation (KDE) was used to reveal the spatial and temporal trends in UER across cities, and a social opportunity function was applied to access the opportunity for economic resilience and the fairness of opportunities for economic resilience in 19 urban agglomerations in China. The results show that the UER was, in general, low across all cities but increased over time. Geographically, the UER disperses from the eastern coast to inland cities. Amongst urban agglomerations in China, the economic resilience opportunity index also varies spatially and increases over time. On the other hand, the opportunity fairness index of UER remained largely stable and substantial inequalities exist across all urban agglomerations, indicating the need for differentiated policy intervention to ensure equality and the sustainable development of the region. The methodology developed in this research can also be applied in other cities and regions to test its re-applicability and to understand the UER in different contexts.

ACS Style

Haichao Yu; Yan Liu; Chengliang Liu; Fei Fan. Spatiotemporal Variation and Inequality in China’s Economic Resilience across Cities and Urban Agglomerations. Sustainability 2018, 10, 4754 .

AMA Style

Haichao Yu, Yan Liu, Chengliang Liu, Fei Fan. Spatiotemporal Variation and Inequality in China’s Economic Resilience across Cities and Urban Agglomerations. Sustainability. 2018; 10 (12):4754.

Chicago/Turabian Style

Haichao Yu; Yan Liu; Chengliang Liu; Fei Fan. 2018. "Spatiotemporal Variation and Inequality in China’s Economic Resilience across Cities and Urban Agglomerations." Sustainability 10, no. 12: 4754.