Disentangling the complex impacts of urban digital transformation and environmental pollution: Evidence from smart city pilots in China

Sustainable Cities and Society, Год журнала: 2022, Номер 88, С. 104266 - 104266

Опубликована: Окт. 20, 2022

Язык: Английский

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How do green energy investment, economic policy uncertainty, and natural resources affect greenhouse gas emissions? A Markov-switching equilibrium approach

Environmental Impact Assessment Review, Год журнала: 2022, Номер 97, С. 106887 - 106887

Опубликована: Авг. 10, 2022

Язык: Английский

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Government intervention, spillover effect and urban innovation performance: Empirical evidence from national innovative city pilot policy in China

Technology in Society, Год журнала: 2022, Номер 70, С. 102035 - 102035

Опубликована: Июнь 16, 2022

Язык: Английский

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Smart city and green development: Empirical evidence from the perspective of green technological innovation

Technological Forecasting and Social Change, Год журнала: 2023, Номер 191, С. 122507 - 122507

Опубликована: Март 23, 2023

Язык: Английский

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Urban planning policy and clean energy development Harmony- evidence from smart city pilot policy in China

Renewable Energy, Год журнала: 2023, Номер 210, С. 251 - 257

Опубликована: Апрель 17, 2023

Язык: Английский

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Do smart cities promote a green economy? Evidence from a quasi-experiment of 253 cities in China

Environmental Impact Assessment Review, Год журнала: 2022, Номер 99, С. 107009 - 107009

Опубликована: Дек. 15, 2022

Язык: Английский

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Uncovering the role of renewable energy innovation in China's low carbon transition: Evidence from total-factor carbon productivity

Environmental Impact Assessment Review, Год журнала: 2023, Номер 101, С. 107128 - 107128

Опубликована: Апрель 15, 2023

Язык: Английский

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Does smart city pilot improve urban green economic efficiency: Accelerator or inhibitor

Environmental Impact Assessment Review, Год журнала: 2023, Номер 104, С. 107328 - 107328

Опубликована: Окт. 20, 2023

Язык: Английский

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Spatiotemporal pattern evolution and influencing factors of green innovation efficiency: A China’s city level analysis

Ecological Indicators, Год журнала: 2023, Номер 146, С. 109901 - 109901

Опубликована: Янв. 12, 2023

Based on employing the global super efficiency epsilon-based measure (GSE-EBM) model to evaluation green innovation (GIE) of 285 prefecture-level or above cities in China during period 2004–2018, this paper combines approaches kernel density estimation, cold hot spot analysis and standard deviation ellipse intuitively describe GIE's spatiotemporal pattern evolution features, then utilizes geographical weighted regression (GWR) explore spatial heterogeneity affecting factors. The results show that: (1) China's urban GIE displayed a fluctuating increasing trend, revealing clearly regional disparities, gradually decreased from Eastern coastal region Central, Western Northeast region. (2) difference exhibited characteristics expansion, polarization, agglomeration with center gravity shifting Southeast (3) In socio-economic factors GIE, GWR effectively identified heterogeneity, improved explanatory ability compared ordinary least squares (OLS) model. (4) indicate that population density, economic development, transportation infrastructure, openness industrial structure played significant impacts there exists impact each influencing factor. findings study can provide valuable references for transformation high-quality development China.

Язык: Английский

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Spatial impact of digital finance on carbon productivity

Geoscience Frontiers, Год журнала: 2023, Номер 15(3), С. 101674 - 101674

Опубликована: Июль 17, 2023

Low carbon productivity has been identified as a key direction for China's future development. As an important driving force economic growth, the question of whether digital finance that is reliant on technology can support development low-carbon urban economy remains unresolved. Based measured by panel data from 201 cities period 2011–2020, this study applies spatial Dubin model and threshold regression to explore impact productivity, yielding following conclusions. First, distribution heterogeneity in eastern region higher than western region, both are characterized high (low)–high (low) dotted agglomeration. Second, significantly improve via two transmission channels: human capital marketization effects. At same time, exerts spillover effect rising local levels will increase neighboring areas. Heterogeneity analysis indicates agglomerations regions more significant. Third, fixed-asset investment positive nonlinear moderating finance, thus improving productivity. When per capita fixed assets does not exceed 682.73 yuan, only limit pulling productivity; when it value, intensified.

Язык: Английский

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