Relationships between Ecosystem Services and Urbanization in Jiangsu Province, Eastern China

Abstract

Ecosystem services are comprehensive and quantitative indicators for describing ecosystem–human interactions. China has experienced rapid urbanization in the past 30 years, which has created a significant impact on regional ecosystem services. However, whether the impact is linear is not clear as yet. In this study, the Jiangsu province, a main body of the Chinese Yangtze River Delta city cluster, was chosen as a case study. Multi-source remotely-sensed geospatial data, including meteorological, land use, vegetation, and socio-economic data, were collected to estimate the total amount of ecosystem services (TESV) and urbanization levels. Subsequently, the relationships between TESV and urbanization indices (i.e., gross domestic product (GDP) per unit area, GPUA; population per unit area, PPUA; and built-up land proportion, BULP) were determined using the Pearson correlation analysis and piecewise linear regression. The primary findings of this study were as follows: (1) There was a distinct spatial pattern in TESV, which gradually increased from west to east with high-value areas located in eastern coastal areas of Jiangsu. Among different land use types, cropland and woodland contributed the most to TESV; (2) The three urbanization indices had spatial patterns, indicating higher urbanization in southern Jiangsu than in central or northern Jiangsu; and (3) Once GPUA and PPUA exceeded threshold values of 3719.55 × 104 yuan/km2 and 744.37 person/km2, respectively, TESV sharply decreased with an increase in these indices. However, the BULP showed a linear and significantly negative relationship with TESV at all values, which indicated that the impacts of economic and population growth on TESV lagged behind that of built-up land expansion. These findings provide a potentially significant reference for decision-makers to rationally enhance regional ecosystem services during rapid urbanization processes.

Predicting the supply–demand of ecosystem services in the Yangtze River Middle Reaches Urban Agglomeration

“…Based on the different dimensions of ESs, we standardized the individual ESs (WY, CR, WR and NPP) with uniform values between 0 and 1 and then added these values together to obtain the total ecosystem service (TES) (Fang et al, 2021a; Li et al, 2019; Peng et al, 2017) for subsequent analysis. The calculation is as followswhere ESi,j is the original value of ecosystem service i in pixel j and ESi,min and ESi,max are the lowest and highest values of ecosystem service i, respectively.…”

“…where APAR (x, t) represents the photosynthetic effective radiation for pixel x absorbed in month t (g CÁ m À2 Ámonth À1 ) and ε (x, t) represents the actual light energy utilization of pixel x in month t (g CÁMJ À1 ). WR and NPP) with uniform values between 0 and 1 and then added these values together to obtain the total ecosystem service (TES) (Fang et al, 2021a;Li et al, 2019;Peng et al, 2017) for subsequent analysis.…”

Strategy of Energy Conservation and Emission Reduction in Residential Building Sector: A Case Study of Jiangsu Province, China

“…Province located in the eastern coastal center of the Chinese mainland (see Figure 2) and one of the most developed areas in China [28]. As with many other Chinese provinces, since the turn of the 21st Century, its economy's continued rapid development [29] has created serious ecological problems [30], which have led to the need to consider the issue of saving energy and protecting the environment when pursuing economic development.…”

Optimization of Ecosystem Services of Shanghai Urban–Suburban Street Trees Based on Low-Carbon Targets

“…The design, planting, and management of street trees in the urban areas and the suburbs greatly differ due to differences in construction concepts in various eras. On the other hand, different population densities lead to differences in demand, which ultimately leads to significant differences in the management goals and finesse of urban-suburban street trees, which may lead to significant differences in the low-carbon contribution of street trees in a given urban-suburban gradient [31,32]. Therefore, to accurately estimate and evaluate the low-carbon contribution of street trees in Shanghai, determine its characteristics and key influencing factors, and propose an optimization strategy for it, this study mainly carried out the following research: (1) the direct and indirect low-carbon contribution of street trees was estimated, and the comprehensive low-carbon effects of street trees in Shanghai were explored; (2) the low-carbon contribution and influencing factors of street trees in two urban-suburban gradient belts were investigated, along with the spatially heterogeneous characteristics; and (3) the key impact of the low-carbon contribution of street trees in Shanghai was analyzed, and an optimization strategy for improving the lowcarbon contribution of street trees in Shanghai's urban and suburban areas was proposed.…”