Spatial Distribution of UNESCO Global Geoparks in China and Its Influencing Factors

As shown in Table 1, the history of UGGp in China can be divided into three phases: preparatory phase, initial phase and development phase.

(1) Preparatory phase (pre-1999)

The protection of geosites in this phase lays a good basis for establishing UGGp. China began to protect geosites in the 1970s. But only a few items were protected, compared with other types of nature reserves. In 1984, the State Council approved the first national geological reserve: the Middle and Upper Proterozoic Stratigraphic Section of Jixian County, Tianjin. In 1987, the former Ministry of Geology and Mineral Resources issued the Regulations on Establishing Geological Nature Reserves. In 1995, the former Ministry of Geology and Mineral Resources issued the Regulations on the Protection and Management of Geosites. As of 1998, China has established 4 national geological reserves and 31 provincial geological reserves.

(2) Initial phase (1999-2005)

In 1999, China officially became one of the pilot countries of the UNESCO Geopark Program. According to the provisions of the UNESCO, only national geoparks are eligible to apply for designation as a UGGp. Therefore, China launched a program to establish national geoparks in 2000. In 2003, The UNESCO released the Guidelines and Criteria for National Geoparks seeking UNESCO's assistance to join the Global Geoparks Network (GGN). Under the Guidelines, China actively promoted the UGGp application. In 2004, the first batch of 8 national geoparks became UGGp. In 2005, another 4 were recognized as UGGp. However, no policy or technical specification on geoparks was issued in this phase.

(3) Development phase (post-2005)

In this phase, China released a series of guidelines, requirements, notices, technical specifications and standards for geoparks, which standardized the development of UGGp. In May 2018, the State Council underwent a major institutional reform. Through the reform, the competent authority of UGGp in China was changed from the Ministry of Land and Resources to the Administration Office of Geosites and Geoparks, Department of Natural Reserves, National Forestry and Grassland Administration. Since then, China has become more professional in the construction of UGGp. In this phase, 2 national geoparks were designated as UGGp each year, except for 1 in 2012 and none in 2007 and 2016.

4.1 Background

There are several defects with the spatial distribution of UGGp in China:

(1) The spatial distribution is out of balance, and basically follows the Hu Line (the straight line between Aihui County, Heilongjiang and Tengchong, Yunnan). Proposed by Chinese geologist Hu Huanyong in 1935, the Hu Line marks a striking difference in the distribution of China's population.According to the Fifth National Census (2000), the region to the east of the Hu Line is home to 94.1% of the total population of China, despite taking up only 43.8% of the country’s land mass.

Out of the UGGp in China, 39 are located to the east of the Hu Line, and only 4 to the west (Figure 4). The non-uniform layout is inconsistent with the distribution of geosites in China.As of 2015, a total of 5,619 important geosites (including 2,433 basic geosites, 3,269 geomorphic landscape sites, and 108 geological disaster sites) and 247 world-class geosites had been discovered across China [26]. Most of them are concentrated in geologically active areas and regions with abrupt changes in landform. In particular, the Qinghai-Tibet Plateau, Taihang Mountains, Hengduan Mountains and Tianshan Mountains boast a variety of valuable geosites. However, UGGp is extremely rare in these resource rich areas.

(2) The UGGpdistribution does not reflect the features of each region. In general, the geosites in Northeast China are mainly volcanic landforms; the southern parts of North China and Northeast China have important geological sections and fossil sites; Southwest China has a wealth of karst landforms and fossils, with glaciers, alpine canyons and inland high-altitude salt lakes on the Qinghai-Tibet Plateau; Northwest China enjoys unique wind-erosion and ice-erosion landforms, and loess is highly developed on the Loess Plateau in Shaanxi, Gansu and Shanxi; East China, including the eastern coastal areas, boast unique landform of the Yangtze River Delta and sea-erosion landform. However, there is no UGGp that fully demonstrates the regional features of the Loess Plateau, Qinghai-Tibet Plateau, Taihang Mountains (Shanxi section), and the eastern coastal areas.

(3) The geosites with value of reference have not become GGN members. Many stratigraphic sections in China are of great value in scientific research, such as the Precambrian continental facies profile in Jixian County, Tianjin, the profile between Paleozoic and Mesozoic strata in Changxing, Zhejiang, as well as the Jinshitan profile in Dalian, Liaoning. In addition, the trove of fossils in Chengjiang, Yunnan records the unparalleled emergence of organisms in the early Cambrain. Unfortunately, none of these geositeshave been developed into UGGp. It is urgently needed to protect and utilize these sites through UGGp construction.

Considering the values of geositesin science, tourism and reference, this paper attempts to verify whether the spatial distribution of UGGp in China is influenced by economy, infrastructure, tourism and government support, and evaluate the degree of influence of each factor.

4.2 Analysis on influencing factors

4.2.1 Model construction

Multiple regression was employed to ascertain the factors that influence the provincial/regional proportion of UGGp in China and their contributions. In traditional regression model, the correlation effect between objects is largely ignored. To ensure the fitting results, the spatial effect was included into the traditional econometric model. There are two commonly used spatial econometric models, namely, spatial lag model (SLM) and spatial error model (SEM).

The SLM is also known as spatial autoregressive model. This model can be expressed as:

$y=\rho W_{y}+X \beta+\varepsilon$   (1)

where, y is the dependent variable; X is the independent variable; ρ is the spatial regression coefficient; β is the parameter vector; W is the spatial weight matrix; ε is the white noise. The spatial regression coefficient measures the degree of spatial interaction between observations, while the spatial weight matrix is constructed by the adjacency principle.

The SEM mainly measures how much the observations are affected by the impact of random errors from other objects. This model can be expressed as:

$y=X \beta+\varepsilon ; \varepsilon=\lambda \beta+\mu$    (2)

where, ε is regression residual vector; W is the spatial weight matrix; λ is the autoregressive coefficient. The spatial weight matrix is still constructed by the adjacency principle; the autoregressive coefficient measures the spatial dependence of observations.

4.2.2 Variable selection

Let y be the provincial/regional proportion of UGGp in China. Then, the y value can be computed by:

$y=N_{i} / N_{t}$   (3)

where, N_i is the number of UGGp in the i-th province/region; N_t is the total number of UGGp in China.

Next, the set of factors affecting the spatial distribution of UGGp in China can be defined as X={R_i, H_i, E_i, F_i, T_i, G_i}, where R is the number of geosites (the number of world-class geosites), H is the terrain (altitude), E is economy (per capita GDP), F is infrastructure (mileage of expressways, national highways and provincial highways), T is tourism (the total number of tourists per year) and G is government support (the local government’s expenditure on geosite protection).

4.2.3 Data sources

The data on the variables of each province/region were collected from the following sources: the number of world-class geosites was extracted from the Geosite Survey Report released by China Geological Survey; the altitude data were downloaded from the database of geospatial attributes based on Google Earth; the economic data were acquired from China Statistical Yearbook 2018, China Statistical Yearbook on Science and Technology 2018, The Yearbook of China Tourism Statistics 2018, the CEInet Statistics Database, and the official websites of provincial bureau of statistics. The data on each province/region were derived from the mean value of each provincial administrative region (here inafter referred to as “province”).

4.2.4 Results analysis

Table 4. Regression results on the influencing factors of provincial/regional spatial distribution

Note: ***, ** and * are the significance levels of 1%, 5% and 10%, respectively; the bracketed values are p-values.

Formulas (2) and (3) were regressed separately. The results of the SEM and the SLM are compared in Table 4. It can be seen that the SEM clearly outshined the SLM in regression coefficient and significance. This means the former model is more convincing.

As shown in Table 4, the regional/provincial distribution of UGGp in China is based on the spatial layout of geosites, but the correlation between the two factors is not yet strong. Government support, tourism and infrastructure make the greatest contributions to the formation of the current regional/provincial distribution of UGGp in China.

The spatial correlation coefficient of government support is 0.947 and significant on the 1% level. Therefore, government support, including policy support, system supply and fiscal input, plays a decisive role in the UGGp construction and application in China, and serves as the main reason for the spatial imbalance of UGGp in different geographical regions.

Moreover, the spatial correlation coefficient of tourism is 0.943 and significant on the 1% level. This means regional tourism can greatly promote the UGGp construction and application. As a result, the local government should regard UGGp as an important part of regional tourism products and brand building, while protecting geosites and the environment.

Table 5. Tourism development of UGGp in China

Note: Number of tourists and tourism income are from statistics in 2018.