Industrial innovation characteristics and spatial differentiation of smart grid technology in China based on patent mining

In China, sports parks, and green spaces are often spatially integrated to realize the multiple functions of shared green spaces and play an important role in the production and living services of its residents. In this article, it is collectively referred to as green sport space (GSS). Whether the distribution of GSS is equal has an important impact on the sustainable lifestyle and the rehabilitation under the pandemic. Based on the POI data of the Shanghai urban area, it is preliminarily found that the areas with extremely high and high production and living densities are mainly distributed in downtown Shanghai. Polarization of the GSS distribution area and the high heat of points of interest can be seen. When the service radius of the GSS in Shanghai is 500, 750, and 1,000 m, the green space ecological service area can reach 2089.08, 3164.62, and 4469.75 km2, covering 26.17, 39.64, and 55.99% of the total area, respectively. The coverage for walking accessibility of GSSs in Shanghai is extremely uneven. Based on network analysis, the overall accessibility of GSS under the walking mode in each residential district fails to meet the standard of a 15-min living circle, with an average of 15.37 min. The evaluation results of this plan demonstrate that Shanghai needs to further provide GSS space for the public in future to improve public wellbeing and diversify sports spaces.

Section: Kernel Density Estimationmentioning

confidence: 99%

Fitness Culture and Green Space Equity: Accessibility Evaluation of Shanghai Communities

Qiu

Zhou

2022

“…The final valid questionnaires were 373. The demographic description of respondents is as follows: (Alvarado et al, 2018): Gender: 54.69% male and 45.31% female (Bai et al, 2021); Age: the average is 40.07, the standard deviation is 10.51 (Caineng et al, 2021); Education: junior college or above accounted for 71%, senior high school or below accounted for 29% (Chen and Zhang, 2021); Region: 59% respondents were from urban area and 41% from non-urban; and Income: the average monthly income is RMB 8445.04, the standard deviation is 4,618.69.…”

Section: Descriptive Statisticsmentioning

confidence: 99%

“…More specifically, the new energy industry can alleviate or even eliminate the tension between human behavior and the ecological environment by improving the utilization rate of energy resources, using low-carbon technology innovation, implementing green products, and controlling the cost of industrial waste treatment (Alvarado et al, 2018;Khan et al, 2021). With the green innovation characteristics of the new energy industry, it is helpful to help and consolidate the expectations of related enterprises in the overall competitiveness while improving profits and environmental performance (Bai et al, 2021). Not only that, but the new energy industry can also realize no pollutant discharge, which brings the energy social cost (health cost and climate cost) to zero, and at the same time reduces the demand for energy.…”

Section: Introductionmentioning

confidence: 99%

A Pilot Assessment of New Energy Usage Behaviors: The Impacts of Environmental Accident, Cognitions, and New Energy Policies

Zhang¹,

Huang²

2022

Literature reviews and interviews with experts showed that new energy would be the future way of life instead of traditional energy. In this study, a questionnaire survey and SPSS model are used to examine the impacts on people’s new energy usage behavior (NEUB) by its cognitions of energy-related environmental accidents (CEREA) and new energy policies (CNEP), as well as other important factors affecting this behavior. The new energy usage behavior examines people’s new energy usage behavior of product usage (NEUBPU) and forecasted payment (NEUBFP). Among the influencing factors, people’s cognitions of energy-related environmental accidents (CEREA), people’s cognitions of new energy policies (CNEP), new energy characteristics (CNEC), new energy advantages (CNEA), and new energy disadvantages (CNED) are examined, and the influence of personal factors on NEUB is also examined. People’s new energy usage behavior of new energy stove (NEUBNES), new energy car (NEUBNEC), and NEUBFP are significantly positively correlated with CNEP; NEUBNEC is significantly positively correlated with CNEA, and NEUBFP is significantly positively correlated with CNEP and CNED. CNEC and CNEP significantly influence people’s new energy usage behavior of household photovoltaic system (NEUBHPS); CNEP significantly influences NEUBNES; age, monthly income, CNEA, and CNEP significantly influence NEUBNEC; age, monthly income, and CNEP significantly influence NEUBFP.

“…Platjouw (2018) applied principal component analysis to evaluate the annual comprehensive strength of marine economic development and analyzed the evolution of the MEG industry and the differences in the regional spatial structure of the marine economy in Norway. Some other scholars are more dependent on environmental economic methods for discussion (Chou, et al, 2020;Bai et al, 2021b;Zakari et al, 2021;Cao et al, 2022;Dagar et al, 2022). These studies were largely based on rich data.…”

Section: Introductionmentioning

confidence: 99%

Sustainability Assessment of Marine Economy in China: Spatial Distributions of Marine Environmental Governance Entities in Shanghai

Yang

Geng²,

Fu³

et al. 2022

The amount of investment in marine environmental governance (MEG) is growing fast in China, which brought the prosperity of environmental treatment entities (e.g., water treatment enterprises and port pollution control institutions). Based on spatial big data, this paper adopted kernel density method, standard deviational ellipse, and nearest neighbor index to explore the spatial distribution pattern of MEG entities in Shanghai from 2005 to 2021. Several conclusions emerged from this study: (1) From 2005 to 2021, the new MEG entities in Shanghai have increased by more than 10 times; (2) The analysis showed that the nearest neighbor index of Shanghai’s MEG entities was larger than 0.2, with Z values all being lower than −30, indicating that the spatial agglomeration characteristics in Shanghai was significant at an alpha level of 1%. (3) Chongming District, Pudong New District, Fengxian District, and Minhang District were the core agglomeration areas. (4) The standard deviational ellipse analysis suggested that the distribution of Shanghai MEG entities from 2005 to 2021 showed an obvious pattern of spreading from the center to both the north and south. This results in an industrial belt along the Huangpu River. This paper argues that the spatial distribution of MEG entities in Shanghai confirms the central place theory, which highlights the network characteristics that combine centrality with spreading to the surrounding areas.