Evaluating the supply and demand of cultural ecosystem services in the Tibetan Plateau of China

Abstract

Context In the Tibetan Plateau (TP), the supply of cultural ecosystem services (CESs) is unique, and the demand for CESs is gradually increasing with rapid urbanization. Evaluating the relationship between the supply and demand for CESs is critical for guiding regional sustainable development. However, due to the difficulty in obtaining empirical data in the high altitude and complex topography of the TP, relevant research is still lacking. Objectives The objective of this study was to develop an approach to address the difficulty of obtaining the empirical data on the TP and to evaluate the relationship between the supply and demand for CESs. Methods Taking the Qinghaihu–Huangshui basin as an example, we combined the SolVES (Social Values for Ecosystem Services) model and social media big data to evaluate the supply and demand for CESs in the TP. Results Our results showed that the combined method can effectively evaluate the supply and demand for CESs in the basin, and can be used for other remote regions. The supply and demand for CESs in the basin exhibited obvious spatial mismatch. Among the two types of mismatch, in the areas of high supply and low demand of CESs, residents’ subjective well-being (SWB) were substantially lower. Being far away from central city was an important reason for the high supply and low demand of CESs. Conclusions Establishing and improving the transportation system connecting central cities with other counties is encouraged to utilize the rich cultural and tourism resources of the TP, as well as enhance the SWB and promote regional sustainable development.

Exploring the response of ecosystem services to landscape change: A case study from eastern Qinghai province

“…The main reason for this lack of data is that it is more difficult to obtain and quantify data on the temporal dynamics of cultural services in the EQHP. Recent studies on cultural services have first considered how to quantify cultural services in the Qinghai-Tibet Plateau (Hou et al, 2022;Zhang et al, 2022a), and there is still a relative lack of research that incorporates the spatial and temporal dynamics of cultural services in the alpine region into ESBs. In future studies, cultural services should be quantified from spatial and temporal perspectives, and their relationship with ESs should be analyzed more comprehensively.…”

Evaluation of Fairness of Urban Park Green Space Based on an Improved Supply Model of Green Space: A Case Study of Beijing Central City

“…He believes that the data themselves have breakpoints and can be graded using this feature of the data. The classification principle of this method is that the variance between groups is as large as possible and the variance within groups is as small as possible [14,15]. In this study, we use Jenks' best natural break method in ArcGIS 10.2 to classify the supply of UPGS enjoyed by the sub-district as low supply, medium supply, and high supply, and the number of residents in the sub-district as low need, medium need, and high need, and assign them to the attribute table of ArcGIS 10.2 as 1, 3, and 5, respectively [14].…”

“…The classification principle of this method is that the variance between groups is as large as possible and the variance within groups is as small as possible [14,15]. In this study, we use Jenks' best natural break method in ArcGIS 10.2 to classify the supply of UPGS enjoyed by the sub-district as low supply, medium supply, and high supply, and the number of residents in the sub-district as low need, medium need, and high need, and assign them to the attribute table of ArcGIS 10.2 as 1, 3, and 5, respectively [14]. Then, we conduct algebraic operations on the supply of UPGS and the need for residents, as in Equation ( 5), and finally generate the supply-demand relationship table (Table 3).…”

“…Then, we conduct algebraic operations on the supply of UPGS and the need for residents, as in Equation ( 5), and finally generate the supply-demand relationship table (Table 3). This study concluded that sub-district with low supply-low demand (Low-Low), medium supplymedium demand (Mid-Mid), and high supply-high demand (High-High) all reach supplydemand equilibrium; sub-districts with low supply-medium demand (Low-Mid), low supply-high demand (Low-High), and medium supply-high demand are under-supplied; and sub-district with medium supply-low demand (Mid-Low), high supply-low demand (High-Low), and high supply-medium demand (High-Mid) are over-supplied [14]. SD = Supply*100 + Demand (5) This study used Baidu's population heat value to represent UPGS visits.…”

“…In general, socioeconomic data and service beneficiary perception survey data are commonly used to evaluate demand indicators [11,12]. With the development of networks and big data, social media data [13], cell phone signaling data [14], etc., are also gradually being used to reflect the real demands of residents for recreational space. However, due to the availability of data, some scholars are still using socio-economic demographic grid data, gross domestic product, and nighttime lighting data to indicate residents' demand [15].…”