Cropland use sustainability in Cheng–Yu Urban Agglomeration, China: Evaluation framework, driving factors and development paths

Journal Pre-proof Cropland use sustainability in Cheng–Yu Urban Agglomeration, China: Evaluation framework, driving factors and development paths Chao Cheng, Yaolin Liu, Yanfang Liu, Renfei Yang, Yongsheng Hong, Yanchi Lu, Jiawei Pan, Yiyun Chen PII:

S0959-6526(20)30739-3

DOI:

https://doi.org/10.1016/j.jclepro.2020.120692

Reference:

JCLP 120692

To appear in:

Journal of Cleaner Production

Received Date: 12 August 2019 Revised Date:

10 February 2020

Accepted Date: 19 February 2020

Please cite this article as: Cheng C, Liu Y, Liu Y, Yang R, Hong Y, Lu Y, Pan J, Chen Y, Cropland use sustainability in Cheng–Yu Urban Agglomeration, China: Evaluation framework, driving factors and development paths, Journal of Cleaner Production (2020), doi: https://doi.org/10.1016/ j.jclepro.2020.120692. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Contributions： ： Design of the methodology was led by Chao Cheng and Yaolin Liu. Suggestions to model design were made by Yaolin Liu, Yiyun Chen, Yanfang Liu. Primary responsibility for collecting data to parameterize the model was born by Renfei Yang, Yongsheng Hong, and Yanchi Lu. Writing of the manuscript and preparation of tables and figures was led by Chao Cheng. Analysis and discuss of calculated results were led by Chao Cheng, Yaolin Liu and Yiyun Chen. Review and Editing of the manuscript were led by Yanfang Liu, Yiyun Chen and Jiawei Pan. All co-authors (Chao Cheng, Yaolin Liu, Yiyun Chen, Yanfang Liu, Renfei Yang, Yongsheng Hong, Yanchi Lu, Jiawei Pan) read and commented on the manuscript, making suggestions on how to condense the narrative, clarify writing, frame the analysis, and interpret findings.

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Cropland use sustainability in Cheng–Yu Urban Agglomeration, China: Evaluation

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framework, driving factors and development paths

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Chao Cheng a, Yaolin Liu a,b, , Yanfang Liu a, Renfei Yang a, Yongsheng Hong a, Yanchi Lu a, Jiawei Pan a, Yiyun Chen a,

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a School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan, Hubei Province 430079, China

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b Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, 129 Luoyu Road, Wuhan, Hubei Province 430079, China

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ABSTRACT: Cropland and its production toward sustainable pattern play an indispensable role in

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supporting a virtuous circle of economy–society–ecology and achieving the Sustainable Development

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Goals. Taking Cheng–Yu Urban Agglomeration, China as a study area, this study developed an evaluation

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framework to conceptualize cropland use into the economy–society–ecology context with an improved

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Ecological Footprint analysis and triangle model. Stepwise linear regression model and GeoDetector were

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then introduced to identify the spatiotemporal driving factors and mechanism of cropland use sustainability.

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Results reveal the following. (1) From 2003 to 2017, cropland use sustainability temporally presents a

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downward trend of inverse S–shape fluctuation with sustainability status transitioning from weak

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unsustainability to strong unsustainability at the turning point in 2008. Cropland use sustainability

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spatially presents the spatial correlation with a significant agglomeration effect. The spatial distribution of

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sustainability status in the east is higher than that in the west. (2) Factors of cropland use sustainability,

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including cropland areas, sown area of farm crops, pesticides consumption, urbanization rate, city

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impact–force and runoff depth interactionally form the multi–dimensional driving mechanism. (3) Sixteen

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cities across the province boundaries can be grouped into three zones, namely, intensive utilization zone,

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protection priority zone and ecological restoration zone with the targeted and differentiated development

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paths accordingly. These findings can contribute to simply understanding and visualizing the status and

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trends of cropland use sustainability and decision–making for national–level urban agglomeration

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construction. 1 / 36

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Keywords: Ecological Footprint; Cropland’s input–output process; Triangle model; Exploratory spatial

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data analysis; GeoDetector; National–level urban agglomeration

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1. Introduction

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Sustainability, as an old but enduring theme, faces a central dilemma: how to preserve the

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eco–environment and the services that satisfy humans while enhancing sustainable consumption and

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production (Lambin and Meyfroidt, 2011). To achieve a better future with more sustainability for all, the

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United Nations proposed parallel goals, such as zero hunger (Goal 2), responsible consumption and

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production patterns (Goal 12), and life on land (Goal 15) in the Sustainable Development Goals (SDGs)

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(UN, 2015). Cropland and its production are critical not only in achieving these SDGs but also in

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satisfying the growing demand for food, multidimensionally affecting eco–environment protection,

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ecosystem health, and urbanization. Over the past few decades, China’s urbanization has experienced a

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dramatic shift, which brings modernization and socioeconomic development, as well as places significant

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stresses on ecosystem (Wang et al., 2018c) and exerts large impacts on environment (Mi et al., 2019), such

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as human–induced land use and land cover change (LUCC). Urban agglomeration emerges as a result of

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high spatial organization of rapid urbanization, whose sprawl is often at the expense of LUCC with

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cropland loss for meeting urban demands (Zhang et al., 2011). Cropland loss is assessed at 30 to 35 times

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the historical rate, and approximately half of urban sprawl have been at the expense of cropland (Bai et al.,

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2014), leading to land competition and threatening the food security (Tramberend et al., 2019). An

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estimate of one–fourth of total global cropland loss will occur in China (Bren d’Amour et al., 2017).

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Additionally, growing populations accompanied by China’s fast–paced socioeconomic transformation and

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shifting diets (He et al., 2018) can force additional cropland input including agrochemical inputs (i.e.,

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fertilizers, pesticides, and herbicides) (Zhang et al., 2017), which potentially imposes an increased burden

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on the eco–environment such as the non–point source pollution of cropland with decreased cropland 2 / 36

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productivity, degraded ecological function and eroded cropland self–resilience (Bai et al., 2015). China has

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suffered a widespread degradation torment of cropland by pollution, with 19.4% of its cropland being

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contaminated (Hou et al., 2018). These situations in China have rapidly emerged recently and presented a

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continuous and increasing tendency for the coming period (Zhang et al., 2011). It is a considerable

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challenge to reconcile cropland use with eco–environmental conservation and socioeconomic development,

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especially in the process of China’s rapid urbanization. Consequently, it is critically important to analyze

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the dynamic of complicated cropland systems, and unveil the trends and driving mechanisms of cropland

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use sustainability so that appropriate paths can be followed toward sustainable land use.

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Sustainable land use has been extensively studied by researchers since the Brunt Land Report of 1987

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(WCED, 1987). By extending the concept of sustainable land use to cropland, an indispensable land type,

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an interrelationship between human and cropland–cropland use sustainability–has been conceptualized.

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Efforts have been made to study cropland use sustainability using evaluation methods by establishing

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index systems or models from different aspects, and executing empirical analyses at scales ranging from

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administrative regions to geographic areas. Evaluation methods by establishing index systems tended to

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depend on mature or prevalent methods such as pressure–state–response (PSR) model (Wang et al., 2018a),

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triangle model (Cheng, 2017), cellular automata model (Zhang et al., 2017) and risk assessment method

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(Qi et al., 2018). Another normally useful method is Ecological Footprint (EF) (Lin et al., 2018; Rees,

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1992; Wackernagel and Rees, 1997), which provides a land–based framework for evaluating sustainability

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by measuring the relationship between human demands for resource consumption as well as pollution

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absorption and natural resources with their services supply to sustain these demands (Bai et al., 2015; Fang

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et al., 2018). Human demand is aggregated into a straightforward composite metric (Cheng et al., 2019;

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Wackernagel and Rees, 1997) based on six main land types of EF, namely, cropland, grazing land, forest

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land, fishing grounds, built–up land and carbon uptake land (GFN, 2018; Lin et al., 2018). Human demand

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can only be sustainable when they lie within an area’s natural resources and their service supply (Świąder

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et al., 2018). EF can simply and didactically gain insights into measuring sustainability. With such

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advantages, EF has been gradually used to assess cropland, as the pivotal nature resource irreplaceably

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providing humanity obbligato products and services.

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Studies on cropland using the EF model usually fit within two main categories. First, based on the

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conventional EF model, cropland’s Ecological Footprint (CEF) and cropland’s ecological capacity (CEC)

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are calculated and combined as relevant sustainable evaluation indexes (Bai et al., 2015; Świąder et al.,

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2018), Together, CEF and CEC reveal an allowable range of sustainability and the extent to which

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humanity exceeds it (Borucke et al., 2013). These indexes can further be developed into other models,

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such as the grey model (Liu and Lin, 2009), to describe the dynamics and driving forces of cropland use

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sustainability. Second, the conventional EF model has been improved from two approaches to recalculate

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CEF and CEC. The first approach is improving the parameter factors of the conventional EF model

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(namely, equivalence factor and yield factor) with the adjusted scale from the Global Footprint Network’s

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national–level (GFN, 2018; Lin et al., 2018) to the sub–national level related to administrative regions or

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geographic areas (Cheng, 2017; Galli et al., 2007). The second approach applies other theories or methods,

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such as energy analysis (Bai et al., 2015; Liu and Lin, 2009) to re–calculate CEF and CEC (Bai et al.,

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2015; Cheng et al., 2019). However, as previous studies on the assessment of cropland use sustainability

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have tended to focus more on agricultural products produced by cropland (Bai et al., 2015), there has been

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less focus on the pollution absorption in the whole input–output (I–O) process of cropland. Thus,

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comprehensive understanding of the functions and I–O process of cropland is needed to bridge the gap

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between cropland supply and human demand to improve cropland use sustainability in the integration of

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economy–society–ecology (Cheng, 2017; Cheng et al., 2019). In addition, few studies on the dynamics

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and driving forces of cropland use sustainability largely center on the dimension of time and space,

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respectively, but they hardly focus on spatiotemporal integration. Moreover, development paths that are

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previously lacking in cross–administrative regions should be followed, especially urban agglomeration,

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which differ from the research regions in relevant studies (Liu and Lin, 2009; Shi et al., 2013; Wang et al.,

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2018b).

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Under such circumstances, this study aims to investigate the dynamics of cropland use sustainability

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and the driving factors and mechanism of such dynamics in Cheng–Yu Urban Agglomeration. Specifically,

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the objectives of this study are (1) to develop an integrated framework for evaluating the dynamics of

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cropland use sustainability on the basis of the improved EF method and triangle model from the

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perspective of cropland’s function and I–O process, (2) to explore the driving factors and driving

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mechanism of cropland use sustainability spatially and temporally by integrating the stepwise linear

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regression model and GeoDetector method, and (3) to propose the development paths of cropland use

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sustainability for “Chengdu–Yu regional integration” on the basis of the spatiotemporal evolution detection,

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cities’ own conditions and relevant policies. Through this study, we try to enrich the research system for

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the evaluation of cropland use sustainability, and contribute to theoretical references for the cropland

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protection implementation and natural asset management of regional development community across

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administrative boundaries.

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2. Study area and data description

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2.1. Study area

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Cheng–Yu Urban Agglomeration, a unique nation–level urban agglomeration and the region with the

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strongest economic foundation and strength in southwest China (NDRCPRC, 2016). The urban

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agglomeration holds basins, mountains and plateaus as its landform types and has a relative elevation

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difference of more than 7000 meters above sea level (Fig. 1). Cheng–Yu Urban Agglomeration is

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characterized by complex topography, frequent disasters and a vulnerable eco–environment. The

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Wenchuan earthquake (Ms 8.0) hit this region with tremendous changes in LUCC and eco–environment. 5 / 36

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Meanwhile, this area is undergoing drastic cropland competition between the cropland protection in the

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national territory protection pattern and land expansion for urban development. This competition is

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particularly evident in the Sichuan–Chongqing Cooperation Demonstration Zone, which is the cumulative

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result of the coordinated development of urban agglomeration. Coordinated and sustainable development

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requires the cities across administrative province boundaries to “aggregate up” into a syncretic region.

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According to the Development Planning of Cheng–Yu Urban Agglomeration, the urban agglomeration

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covers 27 districts (counties) and parts of areas of Kai County and Yunyang County of Chongqing City,

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and 15 cities of Sichuan Province (except Beichuan County, Pingwu County, Wanyuan City, Tianquan

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County and Baoxing County) (NDRCPRC, 2016).

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Fig. 1 Study area of Cheng–Yu Urban Agglomeration, China.

2.2. Data description

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Land use data were derived from the Land Survey Results Sharing Application Service Platform of

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the Ministry of Natural Resources of China and the Annual Land Use Change Survey. The statistical data

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were accessed from the statistical yearbook of Chongqing City and Sichuan Province, and supplemented

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by water resources department, department of agriculture and rural affairs, bureau of statistics in Sichuan

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Province and Chongqing City. Digital elevation model (DEM) data with a resolution of 30 meters were 6 / 36

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collected from the Geospatial Data Cloud (http://www.gscloud.cn/).

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Urban agglomeration is a multi–city aggregation centered on central cities radiating toward the

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surrounding areas (Fang and Yu, 2017). China’s nation–level urban agglomeration is a high–level spatial

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combination form in city–region development (Song et al., 2018). To facilitate spatial analysis and

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improve the reference to regional policies of Cheng–Yu Urban Agglomeration, this research transforms the

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basic study units of Cheng–Yu Urban Agglomeration from the mixed area planned by the Development

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Planning of Cheng–Yu Urban Agglomeration (NDRCPRC, 2016) to the city–level administrative units,

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including the entire area of Chongqing City and 15 cities in Sichuan Province (Fig. 1).

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3. Methods

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We developed an integrated framework to systematically evaluate the status and trends of cropland use

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sustainability and detect its spatiotemporal driving forces. The framework is shown in Fig. 2. We first

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improved the EF model and introduced the triangle model to construct the framework, aiming at evaluating

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the status and temporal dynamics of cropland use sustainability. Its spatial pattern was complementally

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analyzed by exploratory spatial data analysis (ESDA). We then selected the driving forces of cropland use

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sustainability from the dimensions of time and space, and the driving factors were detected by stepwise

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multiple linear regression (SMLR) and GeoDetector.

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Fig. 2 Framework for evaluating the cropland use sustainability based on the cropland input–output (I–O) diagram.

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3.1. Sustainability evaluation framework based on improved EF model and triangle model

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An evaluation framework of cropland use sustainability is established from the perspective of

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cropland’s I–O process (Fig. 2) and functions (Cheng et al., 2019). The I–O process of cropland includes

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anthropogenic and natural inputs, which exert both positive (additional food supply) and negative

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(additional pollution) impacts. Once pollution exceeds the carrying capacity of cropland, sustainability

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inevitably moves toward a negative direction (Bai et al., 2015; Świąder et al., 2018). Nevertheless,

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cropland, as a multi–functional ecosystem, has two categories of function, namely, the productive function

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of agricultural product production and the ecological function of pollution absorption. In this regard, this

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study improves the conventional EF model for comprehensively considering the impact of human demand

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on the cropland from the following aspects: (1) An exclusive CEF account was created that comprises two

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sub–accounts, namely, cropland product–type footprint (CEFP) and cropland ecotype footprint (CEFE). (2)

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On the basis of the nation–specific and area–type–specific actual hectare for the EF analysis of urban

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agglomeration (Cheng, 2017; Galli et al., 2007), CEFP and CEFE were calculated by the conventional EF

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approach (Galli et al., 2007; GFN, 2018) and the introduced Carbon Footprint model of cropland

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ecosystem (Cheng et al., 2019), respectively. Owing to the limited data, the carbon emission generated in

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the I–O process of cropland was screened out for cropland accounting with the principle of short–board

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effect by using cropland’s Carbon Footprint model (Cheng et al., 2019). This procedure can not only

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represent the EF produced for absorbing the contaminants that exceed the carrying capacity of cropland

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but also identify the influences of carbon emission in the process. The accounting of other pollution and

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eco–environmental impacts is expected to be further studied.

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EF model provides a clear outline to evaluate land use sustainability from the perspective of

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supply–demand balance (Cheng et al., 2019). However, EF focuses on the relationship between supply and

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demand rather than the interrelationship among economy, society and ecology (Wackernagel and Yount,

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1998). The triangle model appropriately bridges such a gap of EF with the advantage of easily visualizing 9 / 36

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the basic representation of economy, society, and ecology interaction. The triangle model (Xu et al., 2006;

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Zhou et al., 2017) derives from a classification method for identifying soil texture in soil science; then, the

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model is extensively developed for evaluating sustainability status and trends (Cheng, 2017; Xu et al.,

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2006; Zhou et al., 2017). The triangle plot presentation can be introduced for identifying sustainability

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types and status in that the complicated interrelationship among economy, society, and ecology can be

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simply visualized on a triangle diagram over a long period. In this way, referring to the improved EF

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model (Cheng et al., 2019) and the sustainability assessment framework for agricultural land remediation

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(Hou et al., 2018) and agricultural land use (Qi et al., 2018), the sustainability evaluation framework is

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synthetically established for assessing cropland use sustainability from the perspectives of economy,

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society, and ecology as shown in Fig.2, which contains the following three steps:

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Step1: CEF and CEC are calculated, which are as follows (Cheng et al., 2019):   q AP  CEC = cec × P = S × YF = S × ∑  i × i  api  i  Qi  CEF =cef × P = CEF + CEF P E  q CEF = cef × P = P P ∑i EQi i   CEFE = cef E × P = Qce Pc

(1)

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where cec, cef denote the per capita CEC and CEF, respectively; YF is the yield factor; P indicates the

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population; S is the area of cropland; qi and Qi represent the annual yield of product i in the given region and

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nation, respectively; api and APi denote the sown area of the given region and nation, respectively; cefP is the

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per capita CEFP, EQi is the annual nation–average yield; cefE is the per capita CEFE; Qce and Pc indicate the

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cropland carbon emissions and carbon sequestration capacity, respectively.

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Step2: Three synthetic evaluation indexes are established and calculated. The data matrix is established

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on the basis of the calculation of CEC and CEF. Subsequently, the evaluation index of cropland use

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sustainability is constructed by referring to the existing land use sustainability index (Bai et al., 2015; Cheng,

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2017; Zhao et al., 2011). The cropland use press index (CPI) is constructed from the ecological dimension

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(Cheng, 2017), cropland use supply–demand index (CSI) is constructed from the social dimension (Cheng,

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2017; Liu and Lin, 2009; Zhao et al., 2011) , and the cropland use efficiency index (CEI) is constructed from 10 / 36

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the economic dimension (Cheng, 2017). The evaluation index of cropland use sustainability takes the

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following form:

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CPI = CEF CEC  CSI = cec（cec + cef ） CEI = CEF GDP 

(2)

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where CPI is the cropland use press index, the higher value of CPI denotes higher pressure of human

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induced activities on eco–environment, and vice versa. CSI is the cropland use supply–demand index,

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which ranges from 0 to 1. CEI is the cropland use efficiency index, the lower value of CEI means higher

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efficiency of economies produced on cropland use, and vice versa.

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Step3: A triangle diagram is constructed to identify the status and trends of cropland use sustainability.

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The approach of three synthetic indexes has the advantage of simplicity in that it is possible to introduce a

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triangle as the basic visual display of the three dimensions of ecology, society and economy (Xu et al.,

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2006). The interrelationships among CPI, CSI and CEI are presented in the triangle model to assess the

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status and trends of cropland use sustainability. The triangle model is presented in Fig. 3.

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Fig. 3 Triangle diagram and sustainability status of cropland use sustainability.

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As presented in Fig. 3, the triangle model of cropland uses sustainability displayed as an equilateral

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shape. The X, Y, and Z axes represent CPI, CSI and CEI, respectively. Each axis is divided into five equal

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parts in a counterclockwise direction, and labeled within the range of [0, 1] (Zhou et al., 2017). Depending

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on the relative position produced by the three synthetic indexes, the homologous status and trends of

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cropland use sustainability can then be evaluated. The triangle is classified into five zones, namely, A, B, C,

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D, and E, indicating the five statuses of cropland use sustainability (Fig. 3) (Xu et al., 2006). The relative

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values of CPI, CSI, and CEI are used and diagramed in the triangle model. The sum of the relative values 12 / 36

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of these three indexes are equal to 1 (Xu et al., 2006; Zhou et al., 2017).

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3.2. Exploratory spatial data analysis

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ESDA, as a key technique of spatial statistical analysis, is the useful measurement of studying spatial

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autocorrelation to reveal spatial distribution of objects (Zhang and Wang, 2018). Spatial autocorrelation

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includes global autocorrelation and local autocorrelation, which can be respectively measured by Global

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Moran’s I (GMI) and Local Moran’s I (LMI) (Anselin, 1995) by using Open GeoDa software. First, global

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spatial autocorrelation statistics, namely, GMI, is introduced to describe overall spatial distribution and

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identify whether the distribution of cropland use sustainability has spatial agglomeration (Zhou et al.,

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2018). Second, LMI is further used to explore the spatial location of the agglomeration center and its

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surrounding cities of cropland use sustainability. The formula for calculating GMI and LMI is as follows:

(

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)(

n n  Wij X i − X X j − X ∑∑  i =1 j ≠ i GMI = n n  S 2 ∑∑ Wij  i =1 j =1   Xi − X n  LMI = Wij X j − X ∑ S2  j =i

(

)

(

) (3)

)

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where, Wij refers to the spatial weight matrix; n is the number of cities; Xi and Xj is the cropland use

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sustainability of city i and j, respectively; X denotes the average of cropland use sustainability of all cities;

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and S2 represents square deviation. For characterizing the significance of spatial autocorrelation, GMI and

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LMI can be tested by Z–score (Zhou et al., 2018).

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3.3. Spatiotemporal driving forces analysis

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Time and space are inseparable parts in characterizing research objects or issues in the real world (Xu

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et al., 2019). As a comprehensive issue involving various dimensions, cropland use sustainability is

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characterized by time and space. This study selected the driving forces of cropland use sustainability from

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the dimensions of time and space, combining the indicators existed in relevant studies (Hou et al., 2018;

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Liu and Lin, 2009; Solarin and Al-Mulali, 2018; Solarin et al., 2019; Zhang and Wang, 2018) and using 13 / 36

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actual regional situation. From the time dimension, human activities and eco–environmental states affect

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land use, which is to a great extent manifested as constraining forces driven by eco–environmental,

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socioeconomic factors and the land itself (Long et al., 2008). The temporal driving factors are selected

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from the perspective of cropland production, socioeconomic development and eco–environmental

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endowment. From the space dimension, the socio–economic objects always keep an interactive status in

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the region or space, which is also similar to the basic notion of spatial substance interaction in “pole–axis”

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theory (Song et al., 2018). This theory implies forming a “pole–axis” diffusion mechanism with the city as

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the “pole” and the linear infrastructure as the “axis” (Lu, 2009). Through such a spatial mechanism, urban

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pattern, expansion form and boundary change with LUCC, thus affecting cropland use sustainability (Fang

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and Yu, 2017; Lu, 2002). The spatial driving factors are selected on the basis of “pole–axis” theory. We

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summarized and further developed the spatiotemporal driving forces of cropland use sustainability to

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improve the scientificity and conciseness of the selected indicators. First, according to the existing

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research literature (Liu and Lin, 2009; Qi et al., 2018; Zhang and Wang, 2018), we selected representative

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indicators. Second, we added indicators based on the consulting expert opinions and the actual regional

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circumstances, which shows the authority and professionalism of the chosen indicators. Third, we deleted

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indicators without available data and the distinct features of cropland. By sifting the indicators selected

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above, 21 spatiotemporal driving forces were chosen (shown in Table 1). SMLR was introduced to analyze

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the temporal relationship between temporal driving factors and cropland use sustainability. GeoDetector

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was developed to reveal the spatial characteristics of cropland use sustainability and detect the spatial

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driving factors that lead to this spatial characteristic from the perspective of “pole–axis–agglomerated

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region” (Fang and Yu, 2017).

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Table 1 Indicators of spatiotemporal driving forces analysis for cropland use sustainability. Type

Criteria layer

Indicators

Description

Temporal

Socioeconomic

Per capita GDP(X1)

An important indicator used to measure the overall economic situation of a region.

driving

development

Urbanization rate(X2)

The percentage of the urban resident population divided by total resident population of a region.

Tertiary industry proportion(X3)

The proportional relationship indicates the industrial structure of a region.

Agricultural employment(X4)

The indicator refers to rural labor forces who are engaged in agricultural activities.

Gross output value of farming(X5)

The total value of products of farming, reflecting the economic benefits of farming.

Agricultural machinery power(X6)

The indicator refers to total mechanical power of machinery used in farming.

forces

Cropland production

Proportion of cropland irrigated(X7) Sown area of farm crops(X8) Chemical fertilizers consumption(X9) Pesticides consumption(X10) Eco–environmental endowment

Precipitation(X11)

The ratio of areas that are effectively irrigated and cropland area, which is an important indicator reflects drought resistance capacity of cropland. The area of land sown or trans–planted with crops, indicating the utilization of cropland area. The quantity of chemical fertilizers applied in agriculture, including nitrogenous fertilizer, phosphate fertilizer, potash fertilizer, and compound fertilizer. The quantity of pesticides utilization, representing the agrochemical inputs. The indicator refers to the deepness of liquid state of solid state(thawed) water falling from the sky to the ground that has not been evaporated, infiltrated or run off.

Cropland areas(X12)

The indicator refers to area of land reclaimed for the regular cultivation of various farm crops.

Water resources amount(X13)

The indicator as an important natural resource is indispensable for cropland use.

Green space area(X14)

The area of green space plays a role in improving the eco–environment and quality of stationary life.

Ecological water requirement(X15)

The indicator denotes the eco–environment water supplied by man–made measures and water replenishment of rivers, lakes and wetlands.

Spatial

Pole

City impact–force(S1) (Long and Yang, 2012)

The spatial driving factors are selected on the basis of “pole–axis” theory (Lu, 2002; Lu, 2009). The

driving

Axis

Highway density(S2)

“pole” here refers to various levels and central cities with their radiation range. The “axis” refers to

Runoff depth(S3)

the “infrastructure bunch” linking up by trunk lines of transportation, communication, energy and

Matching coefficient of land and water

water source. With further regional development, “pole–axis” inevitably develops into

resources(S4) (Cheng et al., 2019)

“pole–axis–agglomerated region”. Here the “agglomerated region” are metropolitan groups owning

Sunshine duration(S5)

the regional conditions (Lu, 2009).

forces Regional conditions

Annual mean temperature(S6)

15 / 36

264

3.3.1. Stepwise multiple linear regression (SMLR)

265

SMLR, as an extension of ordinary least–squares linear regression, is used to screen how many

266

variables and evaluate particular variables (Latt and Wittenberg, 2014). In this way, SMLR model was

267

developed to identify and quantify the relationships of cropland use sustainability and its temporal driving

268

factors in Cheng–Yu Urban Agglomeration, from 2003 to 2017. First, the cropland use sustainability

269

obtained from triangle model was transformed into index form. Second, SMLR was selected and tested the

270

factors one by one. Factors with non–significant partial F test were removed, and the remaining factors

271

were selected into the SMLR. This process was repeated until no more factors could be selected or

272

removed to form the best model. SMLR takes the following form: Y = a0 + a1 X 1 + a2 X 2 + L + an X n

273

(4)

274

where, Y is the dependent variable (i.e. cropland use sustainability); X1, X2, …, Xn are the independent

275

variables (i.e. temporal driving factors); and a0 is the constant; ai (1 ≤ i ≤ n) is the standard partial

276

regression coefficient.

277

3.3.2. GeoDetector

278

GeoDetector, as a powerful statistical method, was developed by (Wang et al., 2010) for detecting the

279

spatial heterogeneity of study objects (Zhou et al., 2018). In this study, q–statistic, as the measurement of

280

determinant power revealing spatial heterogeneity in the GeoDetector, was introduced to probe the impacts

281

of spatial driving factors on cropland use sustainability in Cheng–Yu Urban Agglomeration, which is

282

calculated as follows: q = 1−

283

m

1 nσ

2

∑nσ i =1

i

2 i

(5)

284

where, q is the explanatory power of driving factor, which ranges from 0 to 1; n and б2 represent the

285

sample amount and variance, respectively; ni and бi2 are the sample i and its variance.

286

4. Results and discussion

287

4.1. Spatiotemporal characteristics of cropland use sustainability in Cheng–Yu Urban 16 / 36

288

Agglomeration

289

4.1.1. Temporal characteristics of cropland use sustainability

290

The cropland use sustainability in Cheng–Yu Urban Agglomeration presented two status, weak

291

unsustainability (Type

) and strong unsustainability (Type

), showing a downward trend of inverse

292

S–shape fluctuation (Fig. 4) during the study period (2003–2017). In the period of 2003–2007, the status

293

of cropland use sustainability was weak unsustainability (Type

294

a period of rapid change, and the change was relatively large. The change in 2005–2007 was relatively

295

moderate. In the period of 2008–2017, the status of cropland use sustainability was characterized as strong

296

unsustainability (Type V), in which the change direction in 2008–2014 was toward the south in the triangle

297

model. After a temporary change in 2014–2015, the change direction of 2005–2017 returned to the original

298

direction toward the south.

). Notably, the period of 2003–2005 was

299 300 301

Fig. 4 Status of cropland use sustainability in Cheng–Yu Urban Agglomeration from 2003 to 2017.

4.1.2. Spatial characteristics of cropland use sustainability

302

The spatial difference of cropland use sustainability in Cheng–Yu Urban Agglomeration is evident

303

from 2003 to 2017, as shown in Fig. 5. Overall, the cropland use sustainability of 16 cities is generally low, 17 / 36

304

occurring with low sustainability status, namely, weak sustainability (Type III), weak unsustainability

305

(Type IV), and strong unsustainability (Type V). The spatial distribution of cropland use sustainability in

306

the east of Cheng–Yu Urban Agglomeration is higher than that in the west. Specifically, the spatial

307

evolution of cropland use sustainability presents the following characteristics. First, the status of cropland

308

use sustainability in 68.78% of 16 cities is mainly weak unsustainability (Type

309

the north, central, and south areas of the Cheng–Yu Urban Agglomeration. Of 16 cities, 31.22% has weak

310

sustainability (Type

311

Agglomeration. Second, the status of cropland use sustainability in 43.75% of 16 cities has changed

312

presenting an overall downward trend during the entire research period. Among which Leshan has the

313

highest frequency of status change of cropland use sustainability. With the rapid development of urban

314

construction, Leshan’s infrastructure investments have been increasing, especially the transportation

315

infrastructure of Cheng–Gui high speed railway. The linear infrastructure formed an “axis” in the territorial

316

development model of “pole–axis”, which bring rapid socioeconomic development and drive the LUCC

317

with cropland occupation. However, such a situation potentially causes more CEF of Leshan through their

318

instrumental effects on cropland loss and soil pollution. The CEF experienced a fluctuated trend of

319

W–shape during the study period affecting the status of cropland use sustainability. The government then

320

adjusted land use planning and land remediation to curb the situation, which brought about the increased

321

quantity and ameliorative eco–environment of cropland, resulting in the recovery of CEC. The fluctuated

322

change of CEF and CEC inevitably leads to frequent status change of cropland use sustainability in Leshan.

), which is distributed in

), and this proportion is distributed in the east and west of the Cheng–Yu Urban

18 / 36

Fig. 5 Spatial pattern and the evolution characteristics of cropland use sustainability in Cheng–Yu Urban Agglomeration from 2003 to 2017. 19 / 36

339

To further analyze the spatial agglomeration characteristics of cropland use sustainability in

340

Cheng–Yu Urban Agglomeration, as shown in Fig. 5, GMI was calculated and passed the 5% level

341

significance test indicating that the cropland use sustainability is not random in spatial distribution, but

342

similar types of spatial agglomeration characteristics are presented. The agglomeration characteristics

343

become increasingly evident with time changes. LMI was further introduced to demonstrate the interaction

344

between different cities and its impacts in detail by using the agglomeration map of local indicators of

345

spatial association (LISA) (Anselin, 1995; Zhou et al., 2018) with the LISA significance level statically

346

significant at 5%. LISA significant maps are shown in Fig. 5, presenting the agglomeration type of

347

Low–Low clustering (L–L) and High–Low clustering (H–L). The L–L type means that city i is the center

348

of low observation value surrounded by adjacent areas with low observation value (Zhou et al., 2018) with

349

the distribution in Neijiang, Ziyang and Chongqing (Fig. 5). Neijiang is mainly located in the dense

350

metropolitan area of Sichuan Province, and is realizing the new form of urbanization. Urbanization

351

construction can result in cropland occupation by construction land, constraining cropland use

352

sustainability. Ziyang is adjacent to Chengdu under the impacts of the adjustment of administrative

353

divisions between Ziyang and Chengdu and the siphoning effect of the mega city of Chengdu, leading to

354

the limited growth of cropland use sustainability. Chongqing has a large population density and a strong

355

contradiction between population and land. It covers a high proportion of scattered land with more dry

356

land and less paddy fields in the hinterland of the Three Gorges Dam Project. This project is the largest

357

water conservancy project in the world, inundating 1.56 thousand hectares of cropland. Combining with

358

serious soil erosion, cropland use sustainability in Chongqing is steadily low and remains unchanged

359

during the entire study period. The H–L type denotes that the area is the center of high observation value

360

surrounded by adjacent low observation value areas (Zhou et al., 2018) with the distribution in Guang’an

361

and Luzhou (Fig. 5).

362

4.2. Detecting the driving factors of cropland use sustainability 20 / 36

363

4.2.1. Temporal driving factors of cropland use sustainability

364

The SMLR model was developed to eliminate the collinearity factors and screen the driving factors,

365

thus forming the best model (Table 2). The results show that correlation exists between the selected

366

temporal driving factors and cropland use sustainability, with the probability value (P–value) < 0.5, which

367

achieves the significance test at the 5% level.

368

369

Table 2 Models of cropland use sustainability and temporal driving factors Cities

Driving type

P–value

Stepwise multiple linear regression (SMLR) model

Factor type

Chengdu

B

0.012

y=0.484-0.005X9

Solo-factor

Zigong

C

0.000

y=0.609+0.005X12-0.13X15

Dual-factor

Luzhou

AC

0.000

y=0.728+0.001X3-0.001X12

Dual-factor

Deyang

A

0.000

y=0.5404+0.004X2

Solo-factor

Mianyang

BC

0.000

y=0.536+0.012X9-0.009X10

Dual-factor

Suining

BC

0.000

y=2.148-0.0103X7-0.032X12-0.003X13

Multi-factor

Neijiang

A

0.000

y=0.641+0.02X3-0.0001X5

Dual-factor

Leshan

ABC

0.000

y=0.453-0.003X2+3.346X8-0.00001X11+0.00002X14

Multi-factor

Nanchong

ABC

0.000

y=0.815-0.022X1+0.19X10-0.001X12

Multi-factor

Meishan

AC

0.000

y=0.824-0.003X2+0.00002X14

Dual-factor

Yibin

BC

0.000

y=0.489+3.296X8-0.0068X10-0.001X14

Multi-factor

Guang’an

C

0.005

y=0.819-0.002X12

Solo-factor

Dazhou

B

0.006

y=-0.477+3.673X8

Solo-factor

Ya’an

B

0.000

y=-0.497+2.199X8

Solo-factor

Ziyang

B

0.000

y=0.492+2.2X8+0.437X10

Dual-factor

Chongqing

B

0.000

y=0.349+3.26X8

Solo-factor

Note: A, B and C represent the factor type of socioeconomic development, cropland production and eco–environment endowment, respectively.

370

The temporal driving factors obtained from the SMLR model are mainly divided into three types:

371

socioeconomic development, cropland production and eco–environment endowment (Table 2). Luzhou,

372

Deyang, Neijiang, Leshan, Nanchong, and Meishan belong to socioeconomic development driving types.

373

Chengdu, Mianyang, Suining, Leshan, Nanchong, Yibin, Dazhou, Ya’an, Ziyang, and Chongqing belong

374

to cropland production driving types. Zigong, Luzhou, Mianyang, Suining, Leshan, Nanchong, Meishan,

375

Yibin, and Guang’an belong to eco–environment endowment driving types. For further identification of

376

the driving factors, the temporal driving factors in 16 cities are divided into multi–factor, dual–factor, and

377

solo–factor on the basis of the number of driving factors. The overall pattern of the driving factors of

378

cropland use sustainability in Cheng–Yu Urban Agglomeration is dominated by the dual–factor region

379

centered on the vertical direction, with both sides of the region being solo–factor and multi–factor region. 21 / 36

380

Suining, Leshan, Nanchong, and Yibin belong to multi–factor driving cities, Zigong, Luzhou, Mianyang,

381

Neijiang, Meishan, Ziyang belong to dual–factor driving cities, and Chengdu, Deyang, Guang’an, Dazhou,

382

Ya’an, Chongqing belong to solo–factor driving cities (Table 2). The driving factors and their temporal

383

evolution of cropland use sustainability are diverse, given the evident differences. Furthermore, 12 cities

384

have their driving factors as cropland areas (X12), sown area of farm crops (X8), pesticides consumption

385

(X10) or urbanization rate (X2), accounting for 87.5% of 16 cities. The number of cities with cropland areas

386

(X12), sown area of farm crops (X8), pesticides consumption (X10) or urbanization rate (X2) as driving

387

factors are 6, 6, 4, and 3, respectively, accounting for 37.5%, 37.5%, 25% and 18.75% of 16 cities.

388

Therefore, the temporal driving factors of the cropland use sustainability of Cheng–Yu urban

389

agglomeration are mainly cropland areas (X12), sown area of farm crops (X8), pesticides consumption (X10)

390

and urbanization rate (X2).

391

4.2.2. Spatial driving factors of cropland use sustainability

392

GeoDetector was developed to detect the six spatial driving factors affecting the spatial evolution

393

characteristics of the Cheng–Yu Urban Agglomeration, and determine the deterministic intensity of each

394

factor on the spatial characteristics during the study period. In Fig. 6, the explanatory power (q value) of

395

the driving factors on the spatial evolution characteristics of cropland use sustainability in Cheng–Yu

396

Urban Agglomeration changed significantly, thus determining the main factors. Overall, the explanatory

397

power of highway density(S2), sunshine duration(S5), and annual mean temperature(S6) presented a

398

weakened trend and changed apparently. By contrast, the explanatory power of city impact–force(S1),

399

runoff depth(S3) and matching coefficient of land and water resources(S4) presented an enhanced trend

400

with significant changes. At the beginning of the study period, the main driving factors that contributed to

401

the spatial differentiation of cropland use sustainability were highway density(S2), annual mean

402

temperature(S6), and matching coefficient of land and water resources(S4). In the middle of the study

403

period, the main driving factors that determined the spatial differentiation of cropland use sustainability 22 / 36

404

changed from highway density(S2) to city impact–force(S1), but annual mean temperature(S6) and

405

matching coefficient of land and water resources(S4) remained the main driving factors. By the end of the

406

study period, the main driving factors that determined the spatial differentiation of cropland use

407

sustainability changed from annual mean temperature(S6) to runoff depth(S3). City impact–force(S1) and

408

matching coefficient of land and water resources(S4) remained the main driving factors. All these results

409

show that the spatial driving factors of cropland use sustainability have been converted from highway

410

density(S2) and annual mean temperature(S6) to city impact–force(S1) and runoff depth(S3). The

411

explanatory power of city impact–force(S1) and runoff depth(S3) show a growing trend with the increasing

412

q value from 0.2 and 0.28 to 0.564 and 0.462, respectively. Therefore, the spatial driving factors of the

413

cropland use sustainability in Cheng–Yu Urban Agglomeration are mainly city impact–force(S1) and runoff

414

depth(S3).

415 416 417

Fig. 6 Force of the driving factors (q value) of cropland use sustainability in Cheng–Yu Urban Agglomeration from 2003 to 2017.

418

4.3. Driving mechanism and development paths of cropland use sustainability in Cheng–Yu Urban

419

Agglomeration

420

The dominant driving factors of cropland use sustainability in Cheng–Yu Urban Agglomeration are

421

cropland areas (X12), sown area of farm crops (X8), pesticides consumption (X10), urbanization rate (X2),

422

city impact–force (S1), and runoff depth (S3). The study subsequently analyzed the mechanism of such 23 / 36

423

factors. On this basis, the sustainable zones and development paths of cropland use in Cheng–Yu Urban

424

Agglomeration were proposed, providing practical references for the rational function orientation,

425

equitable allocation, and scientific planning of cities in urban agglomeration.

426

4.3.1. Driving mechanism of cropland use sustainability in Cheng–Yu Urban Agglomeration

427

The interaction of multiple driving factors affects cropland use sustainability in Cheng–Yu Urban

428

Agglomeration. The idea of “Driving factors–Driving objects–Driving paths–Driving results” (Wen et al.,

429

2018) is that different factors that act on different objects and produce different results through different

430

paths. Referring to this idea, this study analyzed the driving mechanism of cropland use sustainability in

431

Cheng–Yu Urban Agglomeration across the administrative province boundaries, as illustrated in Fig. 7.

24 / 36

Fig. 7 Driving mechanism of cropland use sustainability in Cheng–Yu Urban Agglomeration.

25 / 36

440

Cropland areas (X12) are one of the decisive factors that affect the CEC as the ever–increasing

441

population and changing consumption patterns highlight human increased dependence on cropland. CEC

442

helps determine whether people are living within or beyond the particular area of cropland from the focus

443

on the relationship between supply and demand to measure the cropland use sustainability (Wackernagel

444

and Yount, 1998). However, in the development of Cheng–Yu Urban Agglomeration, various human

445

activities that place demand on cropland compete for CEC, ultimately affecting cropland use sustainability.

446

Sown area of farm crops (X8) is also a determinant of CEC by the impacts on the potential of cropland

447

and dependences on the actual productivity of cropland. Such cropland productivity reveals the adjustment

448

of agricultural structure and production intensity, playing an important role in the cropland production to

449

meet the supply–demand balance of cropland. Such a balance relationship can eventually affect the

450

cropland use sustainability.

451

Pesticides consumption (X10) is another factor. Pesticides, as one of the main sources of cropland

452

pollutants (Liu et al., 2013), produce residues after used. These residues change the soil permeability and

453

weakens the ability of water and fertilizer protection, growly resulting in cropland pollution and affecting

454

cropland quality, with the occurrence of soil compaction. To absorb such pollution, additional ecological

455

space is consumed with the increasing CEF, which can pose a serious threat to the cropland utilization and

456

the coordinated interactions of human and land, affecting cropland use sustainability.

457

Urbanization rate (X2) is generally recognized as a simple and widely used indicator of urbanization.

458

Urbanization can catalyze socioeconomic and urban growth, with the characteristic of being land centric

459

and expansion (Liu et al., 2018). However, half of this growth has been at the expense of encroaching

460

cropland (Bai et al., 2014), affecting the cropland use sustainability and its growth potential. Moreover,

461

little space for cropland is available for rural China to promote income growth by expanding agriculture

462

actions. As a result, rural labors move to urban areas for development and economic incomes, the cropland

463

lacks labors will be abandoned and left idle, leading to cropland use unsustainability. 26 / 36

464

City impact–force(S1) promotes the close contact between regions with population movement,

465

material circulation and information transmission, leading regions to be in unity with radiating

466

impact–force. Through the “pole–axis” diffusion mechanism (Lu, 2009), regional development can be

467

realized in a progressive development pattern centered on big cities, affecting urban linkages and division

468

of labor and cooperation. As a result of such a regional development pattern, the pattern, expansion form

469

and boundary of cities have changed with the LUCC. This situation is especially prevalent in the land use

470

change by the form of industrial parks, such as Sichuan–Chongqing Cooperation Demonstration Zone,

471

which tends to trigger the encroachment of cropland, the fragmental layout of cropland and the reduction

472

of ecological function in suburban areas, ultimately affecting cropland use sustainability.

473

Runoff depth(S3) induces the soil erosion after scouring cropland can damage the surface soil and

474

nutrients, leading to an insufficient supply of water and nutrients needed for crop growth. As a result of

475

this soil erosion, crops are in stunted growth, ultimately reducing the yield and quality of cropland.

476

Cheng–Yu Urban Agglomeration is noted worldwide for frequent soil erosion. The pollutants accumulated

477

on the surface are washed by precipitation, and the runoff across fields or pavements drives the pollutants

478

overload gradually. Moreover, once such pollution is relocated into cropland, recovering in a short term is

479

difficult. Thus, cropland quality decreases and eventually affects cropland use sustainability.

480

4.3.2. Development paths of cropland use sustainability in Cheng–Yu Urban Agglomeration

481

Upon detection of the driving factors and the driving mechanism of these factors, this study divided

482

the sixteen cities in Cheng–Yu Urban Agglomeration into three zones: intensive utilization zone, protection

483

priority zone, and ecological restoration zone. Development paths of cropland use sustainability in these

484

zones were proposed accordingly, as illustrated in Fig. 8.

27 / 36

485 486

Fig. 8 Function operation of cropland use sustainability and its development paths in Cheng–Yu Urban Agglomeration.

487

Cities in the intensive utilization zone include Chengdu, Chongqing, Neijiang and Guang’an. Faced

488

with the rapid urbanization and inter–city cooperation of Cheng–Yu Urban Agglomeration, cities in this

489

zone attracts amounts of populations and resources, forming a serious siphon effect (Liu et al., 2018) and

490

aggravating the phenomenon of hollow villages in the suburbs, which leads to the contradiction between

491

the increasing population and the decreasing land. The following is sustainable development path in

492

intensive utilization zone. Cropland in these cities should be economically and intensively utilized,

493

combined with the integrated finishing of fields, water, roads, forests and villages by the rational layout of

494

the fields, the integration of scattered cropland and the management of hollow villages. Transferring the

495

consolidation of cropland and reclamation of waste land into cropland is also essential.

496

Cities in protection priority zone includes Zigong, Luzhou, Suining, Meishan, Dazhou and Ya’an. All 28 / 36

497

these cities belong to the key area of cropland protection in the national territory protection pattern

498

(Suining and Meishan) or the area with sensitive and vulnerable eco–environment (Zigong, Luzhou,

499

Dazhou and Ya’an). The sustainable development path of cropland use for protection priority zone is as

500

follows. The cities are supposed to strictly implement the national policy of basic cropland protection and

501

cropland requisition–compensation balance to control the loss and use variation of cropland, especially

502

high–quality cropland. Funds for cropland protection need to be accelerate established to provide financial

503

support for increasing enthusiasm of farmers in cropland protection and improvement. Subsequently,

504

combined with the resource endowments of cities themselves, the concentration areas of high–quality

505

cropland must be built with the aim of optimizing cropland layout.

506

Cities in ecological restoration zone include Deyang, Mianyang, Leshan, Nanchong, Yibin and

507

Ziyang. Deyang and Mianyang were greatly affected by the Wenchuan earthquake, which damaged the

508

cropland ecosystem in earthquake disaster areas, resulting in the damage or loss of cropland and the

509

reduction of environmental carrying capacity and land use security. Other cities belong to the ecological

510

barrier area with the limited cropland use, such as Leshan. Certain cities are driven by cropland pollution,

511

such as, Ziyang, Yibin and Nanchong, covering serious soil erosion whose proportion is 63.10%, 49.68%

512

and 44.98% respectively. The following is the sustainable development path of cropland use for ecological

513

restoration zone. The relationship between land use and ecological construction should be coordinated to

514

strengthen the construction of disaster–resistant capacity of cropland in a timely and orderly manner and to

515

enhance the ecological function of cropland. Moreover, a unified and coordinated mechanism and

516

monitoring system across the administrative boundaries should be established to monitor and evaluate the

517

possible adverse effects of resources occupation, ecological destruction and pollution discharge. The

518

combination of land improvement projects such as slope upgrading should be carried out to prevent and

519

control cropland pollution and restore cropland eco–environment.

520

4.4. Discussion 29 / 36

521

Cropland use sustainability of Cheng–Yu Urban Agglomeration has experienced a downward trend of

522

inverse S–shape fluctuation from 2003 to 2017 with sustainability status transitioning from weak

523

unsustainability (Type

524

interactional impacts between the vulnerable eco–environment and human exploitation activities that

525

intensely use cropland, and by urban agglomeration’s expansion that often occurs on croplands in rapid

526

urbanization process (Bren d’Amour et al., 2017). The status of cropland use sustainability changed from

527

weak unsustainability to strong unsustainability with an inflection point in 2008. On May 12, 2008, the

528

Wenchuan earthquake (Ms 8.0) struck this area, which led to a great disaster. Because of the earthquake

529

and its secondary disasters, 129.5 thousand hectares of cropland were damaged, numbers of urban and

530

rural housing were collapsed or damaged, regional ecological functions were seriously impaired, and

531

environmental carrying capacity were reduced. As an earthquake–stricken area, Cheng–Yu Urban

532

Agglomeration was inevitably affected and its cropland use sustainability has changed dramatically from

533

weak unsustainability to strong unsustainability. After 2008, restoring the collated and destroyed cropland

534

become arduous, highlighting the potential land competition between post–disaster reconstruction and

535

agricultural uses as urban populations grow. The growth of urban agglomeration triggered the construction

536

of infrastructures and facilities such as transportation, energy and water conservancy, exacerbating the

537

encroachment of cropland. Additionally, the eco–environment of this area is complex and is a water

538

conservation area of the Yangtze River Basin. These situations were coupled with a new round of

539

implementing the policy of Grain for Green Program and the coordination of cropland use and

540

eco–environment protection. Both also needed to occupy parts of the cropland, resulting in dilemma of

541

cropland protection and cropland use sustainability. Therefore, the status of cropland use sustainability was

542

generally low and showed an overall downward trend, which is similar to that of the case study

543

investigated in Sichuan Province (Wang et al., 2018b) and Chongqing City (Shi et al., 2013).

544

) to strong unsustainability (Type

) (Fig. 4). Much of this can be explained by

The temporal driving factors of cropland use sustainability in Cheng–Yu Urban Agglomeration 30 / 36

545

obtained from the SMLR model mainly includes cropland areas (X12), sown area of farm crops (X8),

546

pesticides consumption (X10) and urbanization rate (X2). Owing to the complexity and diversity of

547

difference in the natural background, socioeconomic development and policy conditions among different

548

cities, the driving factors show significant spatial and temporal differences (Zhou et al., 2018).

549

Consequently, each city can form its own unique SMLR model according to its own situation. The results

550

detected by GeoDetector show that the spatial driving factors of cropland use sustainability were converted

551

from highway density (S2) and annual mean temperature (S6) to city impact–force (S1) and runoff depth

552

(S3). The explanatory power of city impact–force (S1) and runoff depth (S3) show a growing trend with the

553

increasing q value from 0.2 and 0.28 to 0.564 and 0.462, respectively. Highway density (S2) and annual

554

mean temperature (S6) show a decreasing trend with the decreasing q value from 0.500 and 0.646 to 0.166

555

and 0.283, respectively. This finding indicates that to some extent variation in spatial driving factors and

556

their explanatory power reflect a trade–off relationship among cropland use, socioeconomic needs, civic

557

functional positioning and different urban expansion patterns during rapid urbanization. The role of urban

558

agglomeration in the bud period has not been highlighted. Cropland use sustainability depends more on a

559

natural role, which cannot be separated from temperature, whose initial q value is 0.646. In the

560

development of urban agglomerations, cities in the “pole–axis” model were linked up by linear

561

infrastructures to shape the “axis” (Lu, 2009), especially the rapid growth of highway density, which

562

increased nearly four times during the study period. This dynamic adds pressure to cropland loss and

563

threatens CEC in vulnerable regions. City impact–force expanded in a progressive development pattern of

564

Urban agglomeration often centered on big cities, affecting the spatial pattern of urban expansion, which

565

plays a considerable role in cropland loss. As urban areas expand, cropland near urban areas are subjected

566

to greater land competition between agricultural and urban uses (Bren d’Amour et al., 2017) and increased

567

exposure to cropland pollution aggravated by runoff through its effect on EF (Al-Mulali et al., 2015). This

568

process ineluctably disturbs the fabric and function of cropland ecosystem, resulting in variation of driving 31 / 36

569

factors affecting cropland use sustainability. Simultaneously, from the perspective of “Driving

570

factors–Driving objects–Driving paths–Driving results”, the interaction of driving factors forms the

571

multi–dimensional driving mechanism of cropland use sustainability in Cheng–Yu Urban Agglomeration.

572

Based on the spatiotemporal dynamics of cropland use sustainability, the identification of driving

573

factors and the driving mechanism of these factors, this study proposed development paths of cropland use

574

sustainability (Fig. 8) for Cheng–Yu Urban Agglomeration. These development paths can not only

575

promote post–disaster recovery and reconstruction in restoring ecosystems damaged by the Wenchuan

576

earthquake, but also increase the investment in land consolidation and reclamation in small and

577

medium–sized cities. Such an increase is conducive to improving the conditions of cropland production.

578

Furthermore, the restraint pressure of cropland use in Chengdu and Chongqing can be alleviated by

579

improving the land use structure and optimizing the land layout. These processes can avoid the blind

580

exploitation of the surrounding cities and deconstruction of the eco–environment. The overall coordination

581

of urban and rural areas in the entire region is also affected. On the whole, these development paths is

582

more suitable for the recent development of Cheng–Yu Urban Agglomeration in the next period of

583

2020–2025, which coincides with the Development Planning of Cheng–Yu Urban Agglomeration

584

(NDRCPRC, 2016).

585

5. Conclusions

586

This study provides an integrated framework for assessing cropland use sustainability in Cheng–Yu

587

Urban Agglomeration from 2003 to 2017, allowing for identifying the spatiotemporal dynamics of

588

sustainability status and treads of cropland use. The driving factors with their driving mechanism were

589

detected and analyzed temporally and spatially. The dynamics of cropland use sustainability show a

590

downward trend of inverse S–shape fluctuation with sustainability status transitioning from the weak

591

unsustainability (Type

592

show a spatial distribution of sustainability in the east is higher than that in the west. The overall pattern of

) to strong unsustainability (Type

32 / 36

) at the turning point in 2008. The dynamics

593

driving factors of cropland use sustainability is dominated by the dual–factor region centered on the

594

vertical direction; both sides of the region are solo–factor and multi–factor region. Cropland areas (X12),

595

sown area of farm crops (X8), pesticides consumption (X10), urbanization rate (X2), city impact–force (S1),

596

and runoff depth (S3) are the main driving factors of cropland use sustainability. Cities in Cheng–Yu Urban

597

Agglomeration are grouped into three zones with targeted and differentiated development paths. Chengdu,

598

Chongqing, Neijiang, and Guang’an are grouped into the intensive utilization zone for appropriate

599

development. Zigong, Luzhou, Suining, Meishan, Dazhou and Ya’an are grouped into the protection

600

priority zone for conservation. Deyang, Mianyang, Leshan, Nanchong, Yibin and Ziyang are grouped into

601

the ecological restoration zone for short–term development but long–term conversation.

602

Built on empirical findings, this study reveals the following policy implications. First, it implied that

603

decision–makers should attach great importance to restore the destroyed cropland in the manner of land

604

remediation and land reclamation to increase cropland area, and improve the utilization and output rate of

605

cropland, as the earthquake disaster in 2008 caused the decrease of cropland use sustainability to the

606

lowest degree. Moreover, measures such as phytoremediation and limits of primary sources of

607

contamination (Hou et al., 2018) are key to prevent and control cropland pollution to reduce the CEFE and

608

improve the ecological function of cropland when cropland pollution is “overshoot” (Wackernagel and

609

Yount, 1998) caused by agrochemical inputs and diffused by the spatial driving factors of runoff. With the

610

improvement of cropland–owing ecological function, taking cropland–owing ecological function as the

611

“green heart” and “green belt” (NDRCPRC, 2016) of urban agglomeration to build a cropland ecosystem

612

combining suburban ecology and urban green space is equally essential. Finally, the national policy of

613

basic cropland protection, cropland requisition–compensation balance and high–standard cropland project

614

should be synthetically implemented to contain urban expansion using a well–established planning

615

approach (Bren d’Amour et al., 2017) when the expansion of urban agglomeration affected by city

616

impact–force in the “pole–axis” model of territorial development is at expense of cropland loss. 33 / 36

617

On aggregate, this study first contributes to systematically developing a framework for

618

comprehensive evaluation of cropland use sustainability, taking cropland’s I–O process and functions for

619

resource appropriation and pollution assimilation into consideration. The proposed framework can also be

620

generalized to other urban agglomerations, conceptualize the interrelationship among socioeconomy,

621

cropland use and eco–environment, and make the results more intuitive, spatial and visual. The framework

622

can provide new research ideas and methods for the evaluation of cropland use sustainability that helps

623

government officials perceive the status quo of cropland use sustainability. It also enables the authorities to

624

make corresponding policy adaption and actions based on the estimated results. Second, this study

625

contributes to the extant literatures by exploring the driving forces of cropland use sustainability in

626

dimensions of time and space. The findings of this study are helpful in identifying how the spatiotemporal

627

driving forces affects cropland use sustainability in general instead of focusing on one dimension. Such a

628

contribution can provide a new academic perspective and theoretical reference for the research on driving

629

forces.

630 631

Acknowledgements

632

We acknowledge the editors and anonymous reviewers for their constructive comments and suggestions,

633

and water resources department, department of agriculture and rural affairs, bureau of statistics in Sichuan

634

Province and Chongqing City for their data. This work was supported by the National Natural Science

635

Foundation of China [Grant number: 41771432].

636

References

637 638 639 640 641 642 643 644 645 646

Al-Mulali, U., Choong, W.W., Sheau-Ting, L., Mohammed, M., 2015. Investigating the environmental Kuznets curve (EKC) hypothesis by utilizing the ecological footprint as an indicator of environmental degradation. Ecol. Indic. 48, 315–323. Anselin, L., 1995. Local Indicators of Spatial Association—LISA. Geogr. Anal. 27, 93–115. Bai, X.M., Shi, P.J., Liu, Y.S., 2014. Realizing China's urban dream. Nature 509, 158–160. Bai, X.M., Wen, Z.M., An, S.S., Li, B.C., 2015. Evaluating sustainability of cropland use in Yuanzhou county of the Loess Plateau, China using an emergy-based ecological footprint. PloS one 10, e0118282. Borucke, M., Moore, D., Cranston, G., Gracey, K., Iha, K., Larson, J., Lazarus, E., Morales, J.C., Wackernagel, M., Galli, A., 2013. Accounting for demand and supply of the biosphere's regenerative capacity: The National Footprint Accounts’ underlying methodology and framework. Ecol. Indic. 24, 518–533. Bren d’Amour, C., Reitsma, F., Baiocchi, G., Barthel, S., Güneralp, B., Erb, K.H., Haberl, H., Creutzig, F., Seto, K.C., 2017. 34 / 36

647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694

Future urban land expansion and implications for global croplands. Proceedings of the National Academy of Sciences 114, 8939–8944. Cheng, C., 2017. Study on sustainability of cropland use in Yunnan Province based on modified Ecological Footprint model. Master's thesis. Kunming, China: Yunnan University of Finance and Economics. Cheng, C., Liu, Y.L., Chen, Y.Y., Liu, Y.F., Zhang, Y., Shen, S.S., Yang, R.F., Xu, Z.B., Hong, Y.S., 2019. Diagnosing cropland's allowable range and spatial allocation in China's typical mountainous plateau area: An evaluation framework based on ecological carrying capacity. Sci. Total. Environ. 685, 1255–1268. Fang, C., Yu, D., 2017. Urban agglomeration: An evolving concept of an emerging phenomenon. Landscape. Urban. Plan. 162, 126–136. Fang, K., Zhang, Q., Yu, H., Wang, Y., Dong, L., Shi, L., 2018. Sustainability of the use of natural capital in a city: Measuring the size and depth of urban ecological and water footprints. Sci. Total. Environ. 631–632, 476–484. Galli, A., Kitzes, J., Wermer, P., Wackernagel, M., Niccolucci, V., Tiezzi, E., 2007. An exploration of the mathematics behind the ecological footprint. Int. J. Ecodyn. 2, 250–257. GFN,

2018.

Working

guidebook

to

the

national

footprint

accounts

https://www.footprintnetwork.org/content/uploads/2018/05/2018-National-Footprint-Accounts-Guidebook.pdf (Accessed 20 July 2019). He, P., Baiocchi, G., Hubacek, K., Feng, K.S., Yu, Y., 2018. The environmental impacts of rapidly changing diets and their nutritional quality in China. Nat. Sustain. 1, 122–127. Hou, D.Y., Ding, Z.Y., Li, G.H., Wu, L.H., Hu, P.J., Guo, G.L., Wang, X.R., Yan, M., O’Connor, D., Wang, X.H., 2018. A sustainability assessment framework for agricultural land remediation in China. Land. Degrad. Dev. 29, 1005–1018. Lambin, E.F., Meyfroidt, P., 2011. Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences of the United States of America 108, 3465–3472. Latt, Z.Z., Wittenberg, H., 2014. Improving flood forecasting in a developing country: A comparative study of stepwise multiple linear regression and artificial neural network. Water Resources Management 28, 2109–2128. Lin, D., Hanscom, L., Murthy, A., Galli, A., Evans, M., Neill, E., Mancini, M., Martindill, J., Medouar, F., Huang, S.Y., Wackernagel, M., 2018. Ecological footprint accounting for countries: Updates and results of the national footprint accounts, 2012–2018. Resources 7, 58. Liu, Q.P., Lin, Z.S., 2009. Dynamic analysis and prediction on ecological footprint of Jiangsu's cropland. J. Nat. Resour. 24, 594–601. Liu, Y.L., Wen, C., Liu, X.J., 2013. China’s food security soiled by contamination. Science 339, 1382–1383. Liu, Y.L., Zhang, X.H., Kong, X.S., Wang, R., Chen, L., 2018. Identifying the relationship between urban land expansion and human activities in the Yangtze River Economic Belt, China. Appl. Geogr. 94, 163–177. Long, H.L., Wu, X.Q., Wang, W.J., Dong, G., H,. 2008. Analysis of urban-rural land-use change during 1995–2006 and its policy dimensional driving forces in Chongqing, China. Sensors 8, 681–699. Long, Y.J., Yang, Q.Y., 2012. Study the spatial influence of urban economic in Chongqing. Economic Geography 32, 71–76. Lu, D.D., 2002. Formation and dynamics of the "Pole-Axis" spatial system. Scientia Geographica Sinica 22, 1–6. Lu, D.D., 2009. Objective and framework for territorial development in China. Chinese. Geogr. Sci. 19, 195–202. Mi, Z.F., Guan, D.B., Liu, Z., Liu, J.R., Viguié, V., Fromer, N., Wang, Y.T., 2019. Cities: The core of climate change mitigation. J. Clean. Prod. 207, 582-589. NDRCPRC, 2016. Development Planning of Cheng-Yu Urban Agglomeration https://www.ndrc.gov.cn/ (Accessed 20 July 2019). Qi, X.X., Fu, Y.H., Wang, R.Y., Ng, C.N., Dang, H.P., He, Y.L., 2018. Improving the sustainability of agricultural land use: An integrated framework for the conflict between food security and environmental deterioration. Appl. Geogr. 90, 214-223. Shi, K.F., Diao, C.T., Sun, X.F., Zuo, T.A., 2013. Ecological balance between supply and demand in Chongqing City based on cultivated land Ecological Footprint method. Acta Ecol. Sin. 33, 1872–1880. Solarin, S.A., Al-Mulali, U., 2018. Influence of foreign direct investment on indicators of environmental degradation. Environ. Sci. Pollut. R. 25, 24845–24859. 35 / 36

695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711

Solarin, S.A., Tiwari, A.K., Bello, M.O., 2019. A multi-country convergence analysis of ecological footprint and its

712

Wang, J.F., Li, X.H., Christakos, G., Liao, Y.L., Zhang, T., Gu, X., Zheng, X.Y., 2010. Geographical Detectors‐Based health risk

713

assessment and its application in the neural tube defects study of the Heshun Region, China. INT. J. GEOGR. INF. SCI.

714

24, 107–127.

715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740

components. Sustainable Cities and Society 46, 101422. Song, J.Z., Feng, Q., Wang, X.P., Fu, H.L., Jiang, W., Chen, B.Y., 2018. Spatial Association and Effect Evaluation of CO2 Emission in the Chengdu-Chongqing Urban Agglomeration: Quantitative Evidence from Social Network Analysis. Sustainability 11, 1. Świąder, M., Szewrański, S., Kazak, J.K., Joost, V.H., Lin, D., Wackernagel, M., Alves, A., 2018. Application of Ecological Footprint accounting as a part of an integrated assessment of environmental carrying capacity: A case study of the footprint of food of a large city. Resources 7, 52. Tramberend, S., Fischer, G., Bruckner, M., Harrij, V.V., 2019. Our common cropland: quantifying global agricultural land use from a consumption perspective. Ecol. Econ. 157, 332–341. UN,

2015.

Transforming

our

world:

The

2030

agenda

for

sustainable

development.

https://sustainabledevelopment.un.org/content/documents/21252030%20Agenda%20for%20Sustainable%20Developm ent%20web.pdf (Accessed 20 July 2018). Wackernagel, M., Rees, W.E., 1997. Perceptual and structural barriers to investing in natural capital: economics from an Ecological Footprint perspective. Ecol. Econ. 20, 3–24. Wackernagel, M., Yount, J.D., 1998. The ecological footprint: an indicator of progress toward regional sustainability. Environ. Monit. Assess. 51, 511–529.

Wang, Q., Li, S.Q., Li, R.R., 2018a. Evaluating water resource sustainability in Beijing, China: Combining PSR model and matter-element extension method. J. Clean. Prod. 206, 171–179. Wang, Q., Yi, G.H., Zhang, T.B., Bie, X.J., Liu, D.D., He, D., Xu, J.X., 2018b. Evaluation of cultivated land resource in Sichuan Province based on Ecological Footprint model. Resour. Environ. Yangtze. Basin. 27, 80–87. Wang, Y.T., Li, X.B., Sun, M.X., Yu, H.J., 2018c. Managing urban ecological land as properties: Conceptual model, public perceptions, and willingness to pay. Resour. Conserv. Recy. 133, 21–29. WCED. Our common future. Oxford: Oxford University Press, 1987. Wen, Q., Shi, L.N., Wang, Y.H., 2018. Spatial heterogeneity of multidimensional poverty at the village level: Loess Plateau. Acta. Geogr. Sin. 73, 1850–1864. Xu, C.D., Zhang, X.X., Xiao, G.X., 2019. Spatiotemporal decomposition and risk determinants of hand, foot and mouth disease in Henan, China. Sci. Total. Environ. 657, 509–516. Xu, F.L., Zhao, S.S., Dawson, R.W., Hao, J.Y., Zhang, Y., Tao, S., 2006. A triangle model for evaluating the sustainability status and trends of economic development. Ecol. Model. 195, 327–337. Zhang, Q.B., Yue, D.P., Fang, M.Z., Yu, Q., Huang, Y., Su, K., Ma, H., Wang, Y.H., 2017. Study on sustainability of land resources in Dengkou County based on emergy analysis. J. Clean. Prod. 171, 580–591. Zhang, W.J., Wang, M.Y., 2018. Spatial-temporal characteristics and determinants of land urbanization quality in China: Evidence from 285 prefecture-level cities. Sustainable Cities and Society 38, 70–79. Zhang, X.L., Wu, Y.Z., Shen, L.Y., 2011. An evaluation framework for the sustainability of urban land use: A study of capital cities and municipalities in China. Habitat. Int. 35, 141–149. Zhao, X.G., Pan, Y.J., Wang, S., Yao, H., Cui, W.F., 2011. Dynamic analysis of sustainablity of arable land resources use in Yunnan Province: A new method of ecological footprint based on national hectare. Resour. Sci. 33, 542–548. Zhou, D., Xu, J.C., Lin, Z.L., 2017. Conflict or coordination? Assessing land use multi-functionalization using production-living-ecology analysis. Sci. Total. Environ. 577, 136–147. Zhou, T., Jiang, G.H., Zhang, R.J., Zheng, Q.Y., Ma, W.Q., Zhao, Q.L., Li, Y.L., 2018. Addressing the rural in situ urbanization (RISU) in the Beijing–Tianjin–Hebei region: Spatio-temporal pattern and driving mechanism. Cities 75, 59–71.

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Highlights Pioneering in sustainability estimate with Ecological Footprint and triangle model Factors, mechanism of sustainability are identified by integrating time and space Function–based Ecological Footprint is improved by cropland's Input–Output process Weak unsustainability replaces strong unsustainability as the cropland use status The synthesis contributes to policy implements across administrative boundaries

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: