Introduction

The rural region is a regional concept relative to the city, which refers to the vast rural area outside the built-up area of the city, rural regional system is a rural spatial system with a certain structure, function, and interregional connection, which is composed of the interrelation and interaction of humanity, economy, resources, and environment. (Liu, 2018). In the process of urbanization, both developed countries such as the United States, the United Kingdom, and Japan, which have entered the post-urbanization stage, and developing countries undergoing urbanization, have experienced rural decline caused by weak agricultural infrastructure, the reduction of rural jobs and the loss of labor force (Liu and Li, 2017). In China, rural decline can be considered as the result of the unidirectional flow of population, resources, and other factors between urban and rural areas. For example, the aging of farmers, the marginalization of agriculture, the rural hollowing, and other rural decline problems caused by the loss of quality labor force and non-agricultural land use, which brought new challenges to the development of rural areas (Li et al., 2018). In the process of rural decline, the functional system of rural areas gradually declined, especially in areas with relatively backward economic development. The hollowing out of the rural economy leads to the degradation of social security functions, and the aging of the agricultural labor force leads to the weakening of agricultural production functions and a threat to food security. To this end, China has put forward a rural revitalization strategy, focused on improved rural basic functions and multifunctional systems (Chen, 2020).

Rural multifunction refers to the favorable features of a specific rural area that are beneficial to human beings and nature, generated by regional attributes and other systems in a larger regional system at a certain stage of development. It includes not only the guaranteed functions by which it meets local rural demands but also the support functions it offers the urban system and the cooperative function it shares with other rural systems (Liu et al., 2011; Long et al., 2022). Traditional rural functions were mainly rural activities centered on agricultural production, and production was in the core position (Liu and Liu, 2018). With the emergence of agricultural surplus, changed consumption goals, and increasing awareness of sustainable development, rural areas gradually transformed from being dominated by production value to demonstrating more multifunctional development (Holmes, 2006). Rural areas not only provide food production and economic functions but also have ecological, social, and cultural functions, such as regulating the ecological environment, maintaining political stability, and promoting social development (Wilson, 2010). Rural multifunctional development could not only improve the resilience of the rural regional system but also improve the ability to resist risks and maintain the stability of the rural, regional system (Liu, 2022). Rural population loss would lead to problems such as idle rural houses, inefficient land use, and other phenomena, then rural development faced the challenge of decline and hollowing (Cheng and Chen, 2023; Ma et al., 2019), which harmed the multifunctional development of rural areas.

Generally, rural multifunction arises from the rural geographical environment, location, policy, economy, population, and other factors (Qu et al., 2017). With the global trend of rural decline, the impact of demographic change on rural regional functions cannot be ignored. However, the existing related studies have mainly undertaken comprehensive impact analyses of multiple factors on rural functions, and the population size (Zhang and Zhang, 2022), population aging degree (Qu et al., 2017), and other population-related indicators were used as part of the influencing factors, in-depth research on the effect of demographic change had yet to occur. On the other hand, some studies assessed the impact of demographic change on a single rural function such as agricultural production functions. For example, some studies have found that the decrease in rural population has opposite effects on the ecological environment quality in different regions (Liu, 2022), while others have found that the loss of rural population was not conducive to the development of rural production (Zhang and Chen, 2022) and economic functions (Bowns, 2013). However, the impact of demographic change on rural functions is dynamic and complex, affecting not only individual functions but even the tradeoffs and synergy relationships between functions (Li et al., 2023). At present, the research on the impact of rural population loss on the overall level and trend of rural multifunction is insufficient, and the research on the overall impact of depopulation on rural multifunction should be strengthened.

Chinaā€™s population development is at a significant historical turning point, and new trends and characteristics have emerged as population sizes change (Liu and Lin, 2021), while the impact of depopulation on rural development and food security cannot be ignored. In 2020, Chinaā€™s population reached 1.44 billion: an increase of 0.15 billion since 2000. However, the population of the three provinces in Northeast China decreased from 106.54 million in 2000 to 98.51 million in 2020, showing severe population shrinkage. Heilongjiang Province experienced the greatest population loss, with the total population decreasing by 5.04 million, accounting for more than half of the population shrinkage in the three provinces in Northeast China, so there is an urgent need to reveal the impact of demographic change on rural regional functions. As an important grain commodity and strategic reserve base in the Northeastā€™s black soil region, Heilongjiang Province shoulders the heavy responsibility of ensuring national food and ecological security, understanding the evolutionary patterns of rural agricultural production functions and functional systems in the region was important for rural revitalization and resilient development. At present, the impact of depopulation on rural multifunctionality in Heilongjiang Province is still unclear. For this reason, we selected Heilongjiang Province as our research area and used spatial analysis methods and geographical detector models to uncover the spatiotemporal evolution characteristics and the factors influencing rural multifunction decline in depopulation areas between 2000 and 2020. This provided a theoretical basis and case support for rural multifunctionality research in rural population-shrinking areas, which could provide a theoretical basis for both assessing the impact of population shrinkage in agricultural dominant areas and designing scientific countermeasures to its effects.

Materials and methods

Research framework and study area

The rural, regional system is a complex human-land relationship system, which is composed of the core components, such as natural resources, economic development, and social development, and the outer system, such as development policy, industrialization, and urbanization development level (Liu, 2018). With the process of globalization, urbanization, and industrialization, rural areas provide agricultural, economic, and ecological functions for the outer through the continuous exchange of material, energy, and information (Long et al., 2016). The system and structure of rural multifunction are not static, but constantly change with the changes in human needs, social economy, and policy, so rural multifunction presents structural differences and spatial heterogeneity (Tan et al., 2019). For example, in addition to ensuring the basic functions for local life and production, rural areas will also provide leisure tourism functions (Gu et al., 2019) and cultural inheritance functions (Li et al., 2022a), according to the needs of citizens for rural areas, as well as the characteristics of rural areas. With the development of urbanization, the decline of the rural population, especially the loss of the labor force, has become the most important manifestation of rural decline (Li et al., 2019). Population is one of the constituent elements, whose changes will inevitably disturb the current balance of the rural territorial system and affect the rural multifunctionality. For example, population shrinkage might aggravate the conflict between the agricultural production function and the social security function, which has an impact on the relationship between functions (Li et al., 2023). As shown in Fig. 1, three parts were constructed to deeply reveal the evolution trend and factors affecting rural multifunction in depopulation areas: (1) Based on the demographic change trend and characteristics of the study area, changes in rural multifunctionality were identified as affected by demographic change and the degree of shrinkage. (2) The dominant drivers of rural multifunction decline in depopulation areas and its differences under different population sizes. (3) The interaction effects mechanism of natural, social, and economic factors on rural multifunction decline in the whole and different population size areas during the depopulation.

Fig. 1: Demographic changes in the study area and research framework.
figure 1

The geographical location of Heilongjiang Province in China along with its terrain and demographic characteristics was shown. Among them, Aā€“E represented the various changes in population sizes. Then Population decline occurred to varying degrees within the study area, which might differentially affect the development of rural multifunctional functions.

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The study area was Heilongjiang Province, which is in the worldā€™s black soil belt and the most northern and eastern provincial-level administrative region in China, with a total area of 473,000ā€‰km2 (including Jiagedaqi District and Songling District). It is across a river in the east and north by Russia, bordered by Jilin Province in the south and the Inner Mongolia Autonomous Region in the west. The province has Sanjiang Plain, Songnen Plain, and a key forest area, with the terrain high in the middle and low on both sides. Heilongjiang Province with 45.7% of the provinceā€™s forests covered, is an important ecological barrier in China, maintaining the ecological security of the northeast plains. Heilongjiang province belongs to a temperate monsoon climate, with the average annual temperature between āˆ’4ā€‰Ā°C and 5ā€‰Ā°C. Heilongjiang Province produced 77.63 billion kilograms of grain in 2022, accounting for 11.3% of Chinaā€™s total output and ranking first for 13 years among all the provinces in China.

In recent years, the population of Heilongjiang Province has declined, with the total resident population falling from 36.23 million in 2000 to 31.85 million in 2020 (https://www.citypopulation.de/). 78% of the provinceā€™s counties have experienced population shrinkage, with an average population reduction of 37,000 people per county. As shown in Fig. 1, from 2000 to 2020, the population decline in the study area was more severe in the later period than in the earlier period. To ensure the continuity of the research, it retains the population shrinkage of counties whose populations increased in the early stage of the review period and decreased later, and some counties with discontinuity caused by the adjustment of administrative divisions were excluded. Ultimately, 63 counties were selected to contribute to this research (Fig. 1) and were used to explore the evolutionary characteristics and the factors influencing rural multifunction under population shrinkage.

Data sources and preprocessing

As shown in Table 1, the land use, soil, air temperature, elevation, nocturnal light, and administrative division data used in this paper were obtained from Chinaā€™s Resource and Environment Science and Data Center (http://www.resdc.cn), and the precipitation data were obtained from the National Tibetan Plateau Data Center (https://data.tpdc.ac.cn). Different types of spatial data for the relevant years were extracted according to administrative divisions for the calculation of indicators such as human activity intensity (HAI) and ecological service value. Population data such as total population, rural population, and sex ratio were mainly obtained from the National Population Census (https://www.citypopulation.de/), and data on members of the population more than 65 years old were obtained from the Red-Black Population Database (https://www.hongheiku.com). In this study, the proportion of a countyā€™s resident population who were over 65 years old was used to measure the degree of population aging in the study area. Socio-economic data, such as GDP, grain crop production, and per capita net income of farmers, were mainly obtained from the Heilongjiang Statistical Yearbook and the China Statistical Yearbook.

Table 1 Data and their sources for this study.
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Methodology

Assessment of the comprehensive index of rural multifunctionality

The rural multifunction has the attribute of subjective cognition, and there are significant differences in the division of rural functions based on different perspectives according to the different goals of social development, which can be divided by function commonality into general functions (distinguish between urban and rural areas) and special functions (distinguish between different rural regions) (Liu et al., 2011); According to the functional attributes can be divided into three first-level tiers of ā€˜productionā€™, ā€˜livingā€™, and ā€˜ecologicalā€™ functions, and then subdividing them into second-level functions such as the production of agricultural products, industrial production, residence protection, service protection, and ecological preservation (Yin et al., 2021; Shi et al., 2022). Multifunction is an objective attribute of the rural regional system, reflecting the diverse demands of rural and regional development. In this study, RMF (rural multifunctionality) was the metric used to measure the comprehensive level of rural multifunctionality. Given the territorial characteristics of the study area, we selected the agricultural production function (APF), economic development function (EDF), social security function (SSF), and ecological conservation function (ECF) to construct the RMF evaluation index system (Table 2). APF was mainly to consider the research area as a national grain production and strategic reserve base, with the output capacity of agricultural production activities in rural areas and their ability to provide agricultural by-products for urban and rural residents (Xu and Fang, 2019); Rural areas are important receiving places of spillover function under the development of urban functions, in addition to providing agricultural production functions, rural areas also should develop more nonagricultural activities and promote the income of farmers, and the EDF function was used to measure the economic vitality of rural areas. The SSF, which takes into account the space provided in rural areas for the inhabitants of rural areas to reside and live, plays an essential role in maintaining social stability. The ECF function in this study was used to measure the ability of rural areas to provide ecological goods and ecological services to humans (Liu and Liu, 2018).

Table 2 Indicator system of rural multifunctional evaluation.
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Entropy weight method

Based on the index system we constructed, the rural multifunctional value was evaluated using the multi-factor synthetic evaluation model. The formula for the calculation is as follows:

$${{S}}_{{i}}=\sum \,{{W}}_{{j}}\times {{Y}}_{{ij}}$$
(1)

Where Si refers to the rural multifunctionality score of the ith county, Wj is the weight of indicator j calculated by the entropy weight method (Table 2), and Yij refers to the indicator value of the ith county under the weight of the jth indicator. Ultimately, we used the method of the sum of the mean and 0.5 times the standard deviation of the rural multifunctionality score in 2020 to classify the rural multifunctionality score into high, mid, and low values (Long et al., 2022).

Depopulation degree

In this article, the rate of demographic change was used to identify the types of demographic change in the study area, which expressed the demographic change range at the end of the study area relative to the beginning. Among them, a demographic change rate greater than 0 indicated population growth. A rate of demographic change less than 0 was population shrinkage, and the larger the absolute value, the higher the degree of population shrinkage (Tong et al., 2022). The formula for the calculation is as follows:

$${{PC}}_{{i}}=\frac{{{Pop}}_{{i}1}-{{Pop}}_{{i}0}}{{{Pop}}_{{i}0}}\times 100 \%$$
(2)

Where PCi denotes the degree of demographic change in the ith county, divided into five levels. PCiā€‰ā‰„ā€‰0 denotes population increase; āˆ’5%ā€‰ā‰¤ā€‰PCiā€‰<ā€‰0 denotes slight population shrinkage; āˆ’10%ā€‰ā‰¤ā€‰PCiā€‰<ā€‰āˆ’5% denotes moderate population shrinkage; āˆ’30%ā€‰ā‰¤ā€‰PCiā€‰<ā€‰āˆ’10% denotes severe population shrinkage; and PCiā€‰ā‰¤ā€‰āˆ’30% denotes extreme population shrinkage. Popi1 and Popi0 denote the population of the ith county at the end of the period and the beginning of the period, respectively.

Geographical factor and interaction detector

We used the geographical factor detector to determine the ability of various influencing factors to explain the spatial difference of rural multifunctionality, (i.e., the q-value) (Wang and Xu, 2017). We selected 15 factors related to nature, economy, and society for this paper (Table 3). Each factor was then classified by the k-means clustering algorithm or Equal Interval classification with larger q-values (Li et al., 2022b). Consistent grading methods and grading criteria were used for the same factor in different years and population sizes. The principle governing the factor detector was as follows:

$${q}=1-\frac{1}{{N\sigma }^{2}}{\sum }_{h=1}^{L}{N}_{h}{\sigma }_{h}^{2}$$
(3)

Where q is the value of the explanatory power of each factor, whose value is between 0 and 1. The closer the q-value is to 1, the stronger the explanatory power of the factor for rural multifunctionality. N is the number of research units, h (1, 2, 3, ā€¦., L) denotes the stratification status of each factor, L is the number of layers divided by each influence factor, Nh denotes the layer h (1, 2, 3, ā€¦., L) of the number of cells, and \({\sigma }^{2}\) and \({\sigma }_{h}^{2}\) are the variance of rural multifunctionality for all counties in the study area and the variance of rural multifunctionality for counties in layer h, respectively.

Table 3 Influencing factors of the spatially differentiated characteristics of rural multifunctionality.
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We used the geographical interaction detector to explore the explanatory power of the interaction between two independent factors on rural multifunctionality (Wang and Xu, 2017). We also used an interaction detector to assess whether the combination of factors X1 and X2 enhanced or weakened the explanatory power of the spatial heterogeneity of rural multifunctionality, which we referred to as the relationship between the two factors. As shown in Table 4, their relationships could be divided into the following five categories.

Table 4 Influencing factors of the spatially differentiated characteristics of rural multifunctionality.
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Results and analysis

Spatiotemporal characteristics of the evolution of rural multifunctionality at various degrees of demographic change

The direction of change in rural multifunctionality at different degrees of demographic change

As shown in Fig. 2, from 2000 to 2020, the total county population in Heilongjiang Province transitioned from increasing to decreasing, and the population shrinkage gradually increased. The rural multifunctionality changes and spatial characteristics also varied in different periods of demographic change. From 2000 to 2010, the trend was for population growth, and the counties whose populations increased were mainly distributed on the Sanjiang and Songnen Plains. The counties whose populations decreased were mainly distributed in the southern forest area of Sanjiang Plain and the northern forest area of Songnen Plain and mostly demonstrated only slight shrinkage. In the first half of this period, rural multifunctionality was mainly enhanced, even in cases of medium or severe population shrinkage. However, the largest proportion of counties with enhanced rural multifunctionality experienced a population increase. As the depopulation became more pronounced, fewer counties demonstrated enhanced rural multifunctionality. In this period, rural multifunctionality enhancement was mainly found in the counties of the Sanjiang and Songnen Plains whose population increased. From 2010 to 2020, most of the counties in the study area experienced severe or extreme levels of depopulation, and the counties that had population growth and high size in the early stage rapidly experienced extreme population shrinkage. In the second period, the rural multifunctionality of counties in the study area mainly diminished, with only 3 counties showing an increase in rural multifunctionality. When the population shrank, the rural multifunctionality of most counties was weakened. As depopulation deepened, the counties with diminished rural multifunctionality gradually increased. By contrast, the proportion of the counties with diminished rural multifunctionality was larger among counties experiencing severe and extreme depopulation. To sum up, the rural multifunctionality had declined in depopulation areas. Population growth was conducive to the enhancement of rural multifunctionality, and population reduction was more likely to cause the weakening of rural multifunctionality.

Fig. 2: Spatial and temporal characteristics of rural multifunctionality evolution at various demographic change degrees in Heilongjiang Province from 2000 to 2020.
figure 2

Based on the various demographic change degrees (five levels) in space, the spatial characteristics of rural multifunctionality change (strengthening or weakening) were respectively demonstrated in two 10-year stages, and the structures of rural multifunctionality change (strengthening or weakening) under different demographic change degrees were summarized from 2000 to 2020 in this figure.

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The extent of change in rural multifunctionality under different degrees of demographic change

As shown in Fig. 3, population shrinkage had a significant impact on rural multifunctionality. Rural multifunctionality was enhanced in 74% of the counties that had an increased population. ā€˜Slightly enhancedā€™ accounted for the highest proportion of counties (43%), while ā€˜substantially diminishedā€™ was the smallest, at 0. When the population began to shrink, the proportion of counties with enhanced rural multifunctionality declined, while the number of counties with diminished rural multifunctionality increased. Compared with the population increase, the proportion of counties with substantially enhanced rural multifunctionality decreased by 16% when they experienced slight population shrinkage, and the proportion of counties with substantially diminished rural multifunctionality increased by 15%. At medium population shrinkage, the proportion of counties with significantly enhanced rural multifunctionality decreased to 0, while the proportion of counties with significantly diminished rural multifunctionality increased to 30%. As the population shrinkage grew more severe, the proportion of enhanced rural multifunctionality decreased to less than 20%. The proportion of substantially diminished rural multifunctionality increased and then decreased, while the proportion of slightly diminished multifunctionality continued to increase, with the most serious weakening of rural multifunctionality at severe levels of population shrinkage. One can see that rural multifunctionality continued to weaken as the population shrinkage deepened, but the degree of rural multifunctionality weakness eased at extreme population shrinkage.

Fig. 3: Structural characteristics of rural multifunctionality change at various demographic change degrees in Heilongjiang Province from 2000 to 2020.
figure 3

The change degrees of rural multifunctionality were classified as substantially diminished, slightly diminished, slightly enhanced, and substantially enhanced types to demonstrate their structures under different degrees (five levels) of demographic change in this figure.

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Effects of influencing factors on rural multifunctionality in depopulation areas

Changes in the main influencing factors of rural multifunctionality

In this paper, the explanatory power of influencing factors greater than 0.5 was regarded as identifying the dominant factor affecting rural multifunctionality. As shown in Fig. 4, from 2000 to 2020, the main factors influencing rural multifunctionality significantly changed. In 2000, the dominant factors influencing rural multifunctionality were the population urbanization rate (S2, 0.508) in the social factors and the proportion of black soil area (N3,0.596) in the natural factors. By 2010, as the population increased, the population urbanization rate (S2, 0.562) was the only dominant factor, but it had increased its explanatory power since 2000. Rural multifunctional development benefited from the employment opportunities and infrastructure improvements that urbanization brought to rural areas. In 2020, with the population shrinking, the number of dominant factors affecting rural multifunctionality increased to four. Furthermore, rural multifunctionality was dominated by natural and economic factors, with the highest explanatory power being the population urbanization rate (S2, 0.534) and the influence of economic factors on rural multifunction also became more important. Under the trend of population shrinkage, rural areas might face challenges such as labor shortages, aging populations, and economic hollowing out, then economic and natural factors threaten the multifunctional development of rural areas. During the study period, the number and the explanatory power of dominant factors affecting rural multifunctionality gradually increased in the whole region, but the population urbanization rate played a dominant role. In summary, the main influences on rural multifunctionality were becoming more diversified as the degree of population shrinkage increased.

Fig. 4: Explanatory power ranking of factors influencing rural multifunctionality in Heilongjiang Province from 2000 to 2020.
figure 4

The effects of social, economic, and natural factors on rural multifunctionality were shown as symbols of different shapes, which were sorted from high to low according to the magnitude of the effects in the whole study area and three subregions with different population sizes from 2000 to 2020 in this figure.

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As shown in Fig. 4, the explanatory power of the influencing factors for the whole region was weaker than that of different population sizes during the study period, and the dominant factors differed at each population size. In the low population size area, the seven dominant factors were dominated in 2000 by natural and economic indicators, with the highest explanatory power mean elevation (N1, 0.84). In 2010, the proportion of cultivated land (N4, 0.81) explanatory power was the highest but was dominated by economic factors. In 2020, the number of dominant factors dropped to five, and the explanatory power also decreased, at which time the explanatory power of nighttime light (E3) increased to 0.71. Rural areas with small population sizes tend to be relatively rich in per capita resources, which will directly affect agricultural production and rural functions. Economic development and structural transformation had an increasing impact on rural areas, and the explanatory power of economic factors had increased significantly. In the medium population size area, only three factors played dominant roles in 2000: population mobility (S3, 0.72), mean precipitation (N2, 0.69), and population urbanization rate (S2, 0.66). In 2010 the dominant factors were reduced to two, which were human activity intensity (E5, 0.6) and the proportion of cultivated land (N4, 0.55). By 2020, the number of dominant factors increased to seven, with natural factors being particularly important. Initially, medium population size areas had a demographic dividend, and rural multifunction was more influenced by demographic factors. However, as the local economy developed, the demand for cultivated land increased with urban expansion, and rural production space was crowded out, making natural factors the key factor affecting rural function and sustainable development. When the population size was high, there were two dominant factors: nighttime light (E3, 0.56) and mean precipitation (N2, 0.58) in 2000. In 2010, the proportion of cultivated land (N4, 0.57) and nighttime light (E3, 0.54) were the dominant factors. The number of dominant factors increased in 2020, and the proportion of black soil area (N3, 0.78) and road density (E4, 0.69) increased significantly. The state of economic development and natural resources in areas with large population sizes had always affected rural functions. In general, the study area was agriculture-dominated, and natural factors played an important role in the development of rural multifunction. However, the change in population size would change the human-land relationship in the region, making economic factors also have a significant impact on rural function. But in the region with a medium population size, such impact of factors was slight.

Interactions between factors influencing rural multifunctionality in various sub-regions

As shown in Fig. 5, from 2000 to 2020, all interactions among factors across the whole region and in regions of different population sizes showed enhanced relationships. This included two types of two-factor enhancement and nonlinear enhancements, without weakening or independent effects. Specifically, the explanatory power of the interaction between social, economic, and natural factors was significantly stronger than that of a single factor. For the whole region, the differences in the explanatory power of the interaction factors increased during the study period along with the process of demographic change. In 2000, the interaction of economic structure and the proportion of cultivated land (E2 & N4) had the strongest explanatory power for spatial heterogeneity in rural multifunctionality, at 88.88%, while the weakest interaction explanatory power was 37.37%. In 2010, the population mainly increased, the explanatory power of the interaction of nighttime light and the proportion of cultivated land (E3&N4) was the strongest (88.68%), and the population density and mean temperature (S1 & N5) were the weakest (23.31%). The difference of interaction effect between factors was expanded. In 2020, the population size decreased significantly, and the explanatory power of the interaction between economic structure and the proportion of cultivated land (E2 & N4) rose to 94.84%. The interaction of the aging population and the proportion of cultivated land (S5 & N4) had the strongest explanatory power in this stage, reaching 95.55%, and the explanatory power of the interaction was the weakest (35.05%). The interaction of economic and natural factors in rural multifunction has increased from 2000 to 2020, and the differences in the factor explanatory power of the interaction have gradually increased, and the mechanism of influencing factors tends to be more complex.

Fig. 5: Interactions of factors affecting rural multifunctionality in the whole area and sub-region from 2000 to 2020.
figure 5

This figure showed the explanatory power of the interaction effects of social, economic, and natural factors in the whole study area and three subregions with different population sizes from 2000 to 2020. The deeper the red, the stronger the explanatory power.

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In terms of different population sizes, at the initial stage, the interaction of factors was extremely strong in areas with small population sizes, such as the interaction between road density and the proportion of cultivated land (E4 & N4) having an explanatory power of close to 100%. The interaction between natural, social, and economic factors had a strong impact on rural multifunctionality. However, there were some differences in the explanatory powers of the interactions, which were significantly weakened along with the trend of population shrinkage. By 2020, the explanatory power of these interactions between road density and the proportion of cultivated land (E4 & N4) had decreased to 69.75%, and the explanatory power of the economic structure and the proportion of black soil area (E2 & N3) was the highest explanatory power at 99.72%. The multifunctional countryside was more affected by the interaction between economic and natural factors, and the diversity of factors influencing rural multifunctionality decreased. For areas with medium population size, the effects of the interactions also were differentiated, but the fluctuation in their explanatory power with changes in population size was small, for example, the explanatory power of the interaction between road density and the proportion of cultivated land (E4 & N4) on rural multifunctionality was 97.14% in the early period, in the middle period was 87.10%, and in the late period was 95.41%. That is, the effects of interactions of the influencing factors were relatively stable in areas with medium population size. However, the increasingly serious differentiation in the interaction effects of social, economic, and natural factors was observed in areas with large populations. With the population loss, the explanatory powers of the interactions have increased. For example, the strongest explanatory power of the interaction was 96.77% in 2000, while the explanatory power of the interaction between road density and population urbanization rate (E4&S2) reached 100% in 2020. In general, the rural multifunctionality in the study area was influenced by a variety of factors. With population shrinkage, differentiations in the effects of factor interactions were influenced by demographic fluctuations and varied across regions with different population sizes. In a word, for areas with less or more population, the interaction effect on rural functions was more obviously affected by population shrinkage. Therefore, the impact of natural, social, and economic factors and their interaction in areas of population shrinkage should be paid more attention to, and the stability and enhancement of rural versatility under the impact of population shrinkage should also be valued to promote the sustainable development of rural areas.

Discussion

Not only in China but also in many countries in the world, rural population loss is a common problem in the process of urbanization (Liu and Li, 2017). As one of the manifestations of rural decline (Li et al., 2019), population hollowing will inevitably affect the development of rural multifunction. With the loss of the rural population in the process of urbanization, rural decline was a possible problem for global rural development, but the trend of rural recession was not irreversible (Li et al., 2019). For example, it was found that survival pressure forced farmers to develop multiple uses and functions of rural areas, showing that marginal areas with small populations had stronger rural multifunctionality (Willemen et al., 2010; HrabĆ”k and KonečnĆ½, 2018). Timely measures should be taken to deal with the rural development dilemma caused by excessive population loss and alleviate the economic decline in rural areas. Therefore, the first step was to accurately grasp the trend of demographic change, and the gradual transition of population shrinkage in our study area from forested areas with a small population size to plains areas with a large population size. Then, an in-depth understanding of the impact of demographic change on rural multifunction development was needed to promote social stability and sustainable development in the countryside. In 2022, the natural growth rate of Chinaā€™s total population was āˆ’0.60ā€°, that is, the inflection point of population growth might have been reached, with population decline becoming universal in China. How to scientifically deal with population shrinkage and give full play to the advantages of rural multifunction to improve rural decline while focusing on urban development is the focus of future rural revitalization. Therefore, it is urgent and necessary to reveal the evolution law and influencing factors of rural multifunction in shrinking population areas.

We found that population shrinkage contributed to the weakening of rural multifunction in our study area. During the process of depopulation, production functions, living functions, and ecological functions weakened in the rural areas of Heilongjiang Province (Ni et al., 2022). However, the weakening of the multifunctional countryside alleviated in the case of extreme population shrinkage, which may be attributed to significant reductions in labor forcing the vanishment of traditional agricultural economic activities and a gradual shift toward multifunctional development (VerkulevičiÅ«tėā€Kriukienė et al., (2018); He et al., 2020). The weakening of rural multifunction caused by population shrinkage poses a serious challenge to the sustainable development of rural areas, which not only affects the regional stable supply of agricultural products but also may lead to the degradation of the ecological environment and quality of life. In addition, factors influencing rural multifunction in areas with smaller or larger population sizes fluctuated more strongly than medium size with the population shrinkage, indicating that moderate population size was suitable for rural multifunctional development. Therefore, attention should be paid to the population dynamics to minimize the impact of population shrinkage on rural multifunction, especially for rural areas with the main function of agricultural production and the important task of ensuring food security. At the early stage of slow population loss, in addition to maintaining the agricultural production function, it is necessary to increase employment to retain the population as much as possible, and actively improve infrastructure construction, such as providing education and medical resources to attract population return. When the population shrinkage becomes more and more obvious, the adverse impact of depopulation on rural multifunction should be taken seriously, as well as the new possible opportunities for the transformational development of rural multifunction. For example, the green transformation of the agricultural industry could be adopted to strengthen production functions and achieve sustainable development in rural areas. At the same time, the natural advantages could be used to develop the rural tourism industry or special agricultural products to undertake urban functions and realize the flow of urban and rural elements, then to make up for the shortcomings of the economic and social security functions and promote the multifunctional development of rural areas.

Population urbanization rate was the most important factor influencing rural multifunctionality during our study period, which was because the unidirectional outflow of resources and unbalanced urban-rural relations constrained the multifunctional development of rural territories during the progress of surplus labor shifted to non-agricultural industries. However, the main factors influencing rural multifunctionality have become increasingly diversified with depopulation. In addition to the urbanization rate, natural factors have consistently played an important role in affecting rural multifunction under different population sizesā€”a finding that differed markedly from the conclusion arrived at by Yang et al. (2019) that ā€œrural functionality will be mainly affected by socioeconomic factors, and the impact of natural factors on rural multifunctionality was getting smallerā€. This is because, in the important commercial grain production bases, natural factors, such as precipitation and temperature, will affect the suitability of agricultural production and land use structure in the region, which has an impact on rural multifunction. Heilongjiang Province is responsible in part for national food security, which results in the enhanced effect of natural factors on rural multifunctionality. On the one hand, climate warming would increase agricultural production area (Liu et al., 2022), as well as greenhouse gas emissions which is thought to be the main reason for global warming (Huber, Knutti (2012)). On the other hand, climate warming might lead to more frequent pests and diseases, and more frequent and intense extreme weather. As a result, the increase in farmland areas would lead to increased carbon emissions, which reverse affects climate change. Then the stability of the farmland ecosystem was reduced (Wang et al., 2023), which led to the fluctuation of rural multifunction. Moreover, natural factors and economic, and social factors interacted to affect rural multifunction in the study area, and interaction effect differences of influencing factors became larger in the process of population shrinkage. To sum up, to promote multifunctional development in rural areas with shrinking populations, we must pay attention to the changing trend and interaction effects of natural, social, and economic factors influencing rural multifunction.

Conclusions

To reveal the characteristics of rural multifunctional decline and the role of influencing factors in demographic shrinkage areas, mathematical and statistical analysis, spatial analysis, and a geographical detector model were used. This paper analyzes the evolution characteristics of rural multifunctionality under the influence of population shrinkage from two aspects of changes in population size and degree, and the role of influencing factors from two dimensions of whole and population size partitioning. The results showed that, from 2000 to 2020, the population in the study area showed a shrinking trend, which gradually aggravated in the later period. Population growth was conducive to the enhancement of rural multifunctionality, while rural multifunctionality gradually declined with demographic shrinkage. The impact of demographic shrinkage on rural multifunctionality displayed an inverted ā€œUā€ shape when graphed.

In different periods of demographic change, the factors affecting rural multifunction in the study area varied significantly. In the period of population growth, only the urbanization rate belonging to the social factor was the dominant factor, while in the demographic shrinkage period, natural and economic factors were increasingly important. As depopulation deepened, the factors influencing rural multifunction became increasingly complex. The effects of factors influencing rural multifunctionality in areas with various population sizes were quite different from the whole scale. Overall, natural factors played an important role in impacting rural multifunctionality under different population sizes but changes in population size would change the regional situation and increase the impact of economic factors, especially in areas with smaller or larger population sizes.

During the study period, the interaction among natural, economic, and social factors enhanced the influence of a single factor on rural multifunction in Heilongjiang Province. Along with the trend of demographic shrinkage, the interaction among factors gradually differentiated, and the influence of the interaction between economic and natural factors enhanced, while the explanatory power of other interactions weakened. The interaction among natural, economic, and social factors was also different in various population sizes, too. Among them, the interaction between factors affecting rural multifunction was more stable in medium population size areas along with depopulation.