Monitoring Land-Use Efficiency in China’s Yangtze River Economic Belt from 2000 to 2018

Abstract

Monitoring of the indicator Sustainable Development Goal (SDG) 11.3.1 is important for understanding the coordination between land consumption rate (LCR) and population growth rate (PGR). However, the spatiotemporal indicator SDG 11.3.1 changes at the urban agglomeration (UA) level, and the relationship between LCR and PGR in the prefecture-level cities from different UAs remains unclear. In this study, we monitored the spatiotemporal indicator SDG 11.3.1 in the Yangtze River Economic Belt (YREB) and its three major UAs (i.e., Chengdu–Chongqing (CC), the Middle Reaches of the Yangtze River (MRYR), and the Yangtze River Delta (YRD)) for the periods 2000–2010, 2010–2015, and 2015–2018, using the space–time interaction (STI) method and Pearson’s method. Our major findings were as follows: (1) Compared with the world average of 1.28 for LCRPGR (i.e., ratio of LCR to PGR), except for the LCRPGR of the YRD (2000–2018) and CC (2000–2010), the LCRPGR of CC, the MRYR, and the YREB was lower than 1.28 during 2000–2018. (2) The gaps in both population and built-up area between the YREB and the three UAs did not narrow, but widened. (3) Compared with the LCRPGR in China, except for the LCRPGR of the YRD (2000–2018) and CC (2000–2010), the LCRPGR of the YREB increased from 1.21 to 1.23 between 2000–2010 and 2010–2015, and then decreased to 1.16 in 2015–2018, indicating that the relationship between LCR and PGR in the YREB is relatively stable. (4) A significant positive relationship (p < 0.001) was found between LCR and PGR in CC, the MRYR, the YRD, and the YREB. We conclude that the indicator SDG 11.3.1 is a helpful tool for evaluating land-use efficiency caused by the LCR and PGR at the UA level. Our results provide information support for promoting sustainable and coordinative development between LCR and PGR.

1. Introduction

To address global challenges [1], 193 countries have committed to the 17 Sustainable Development Goals (SDGs), supported by 169 targets [2]. SDG 11 aims to make cities and human settlements inclusive, safe, resilient, and sustainable [3], and the indicator SDG 11.3.1—the ratio (LCRPGR) of the land consumption rate (LCR) to the population growth rate (PGR)—can be used to monitor the land-use efficiency (LUE). Understanding LUE is critical to developing appropriate land policies and population policies, or filling gaps in existing policies.

There are many reports and articles on LCRPGR monitoring at the global [4,5], regional [6,7], national [8], and local [9,10,11,12,13,14,15] levels. As highlighted by Li et al. [6], the procedures used to assess the SDG 11.3.1 indicator differ from city to city. For example, the UN Sustainable Development Goals Report 2017 (UN SDG report 2017) reports that the world average for LCRPGR increased from 1.22 during the period 1990–2000 to 1.28 during the period 2000–2015 [16,17,18]. Estoque et al. [4] showed that the mean LCRPGR decreased by 24%, from 1.34 during the period 1975–2000 to 1.02 during the period 2000–2015. The Big Earth Data in Support of the SDGs (2020) reported that the LCRGR of 433 cities in China increased from 1.33 during 1990–1995 to 2.15 during 2010–2015, and that since 2015 the speed of urban sprawl has slowed down.

The LCRPGR values of sampled cities worldwide in developed countries were 2.1 and 1.9 in 1990–2000 and 2000–2015, respectively. During the same periods, the LCRPGR of 194 stratified sample cities around the world was 1.68 and 1.74, respectively [16]. The LCRPGR values in China were 1.69 and 1.78 during 1990–2000 and 2000–2010, respectively [8]. The SDG 11.3.1 indicator was 0.115 in Poland and −0.054 in Lithuania during 2000–2018, where Poland had an LCR of 0.0462 with a PGR of 0.0132, while Lithuania had an LCR of 0.0067 with a PGR of −0.0067 [19]. From 2007 to 2019, the average LCRPGR in Ethiopia was 1.44, with low land-use efficiency. The average LCR was 6.02, and the average PGR for all cities in Ethiopia was 4.63 [20]. In the four periods of 2000–2005, 2005–2010, 2010–2015, and 2015–2020, the LCRPGR values of Opole were −2.45, −5.88, −5.84, and −4.00, respectively. The highest average growth rates for built-up areas occurred between 3-6 km (11.7%) and more than 6 km (10.4%) from the city center [21]. According to Koroso et al. [22], Addis Ababa’s LCRPGR was 1.02 during the period 2005–2019, whereas the LCRPGR values of the sub-city the city’s total LCRPGR values were 3.16 during the period 2011–2019 and 3.62 during the period 2007–2019. Therefore, there are some discrepancies caused by different regions and geographical scales (i.e., global, regional, national, and local) [23].

The Yangtze River Economic Belt (YREB) has grown rapidly in recent years [24]. Liu et al. [24] showed that the urban expansion in the YREB dropped to 13.39% during the period 2005–2010 and 13.38% during the period 2010–2015. In three urban agglomerations (UAs) (i.e., CC, the MRYR, and the YRD), the increase in urban land in the YRD was higher than in the MRYR and CC from 1995 to 2015, due to the YRD’s natural resources and economic circumstances [24]. In addition, China’s LCRPGR and its spatiotemporal changes from 1990 to 2010 have already been analyzed [8]. Wang et al. [8,25] showed that the mean LCRPGR in China increased from 1.69 during the period 1990–2000 to 1.78 during the period 2000–2010 [8,25].

However, spatiotemporal LCRPGR changes, LUE, and the relationship between LCR and PGR at the UA level are either not completely studied, or are missing; lacking this information and knowledge hinders our ability to build policies towards sustainable development for the future at the UA level [26,27].

In this study, we employed the indicator SDG 11.3.1, the space–time interaction (STI) method, and Pearson’s method of LCRPGR to quantify the spatiotemporal LCRPGR changes, LUE, and the relationship between LCR and PGR across the YREB and its three major UAs (i.e., Chengdu–Chongqing (CC), the Middle Reaches of the Yangtze River (MRYR), and the Yangtze River Delta (YRD)) for 2000–2015 and its three sub-periods (i.e., 2000–2010, 2010–2015, and 2015–2018). This work also can help fill the gap in SDG indicator 11.3.1 at the UA level. Therefore, the specific objectives of this study are to (1) illustrate the tends of built-up expansion and population growth in CC, the MRYR, the YRD, and the YREB during 2000–2018; (2) explore the spatiotemporal LCRPGR changes in CC, the MRYR, the YRD, and the YREB for three periods (2000–2010, 2010–2015, and 2015–2018); and (3) quantify the relationship between LCR and PGR in the prefecture-level cities. Our results provide information support for promoting sustainable and coordinative development between LCR and PGR, and also have important implications for spatial projections of future urban expansion.

2. Materials and Methods

2.1. Study Site

The YREB encompasses two municipalities and nine provinces (Figure 1). It covers roughly 21.3% of the total Chinese area, and has a population of almost 600 million, contributing more than 44.3% of China’s GDP. The YREB is one of the fastest-growing regions [28,29], and consists of three major UAs: the Yangtze River Delta (YRD), the Middle Reaches of the Yangtze River (MRYR), and Chengdu–Chongqing (CC). Rapid urbanization and extensive economic activities in the YREB can reflect the various characteristics and patterns of LUE [24,30].

2.2. Data Used

Three datasets, including raster maps of built-up area and population data, and shapefile maps of boundaries of YREB data, were used in this study. The land use/land cover (LULC) type of “Built-up land” in China’s Land-Use/Cover Datasets (CLUDs) (spatial resolution: 100 m) for 2000, 2010, 2015, and 2018 was downloaded from the website of the Data Centre for Resources and Environmental Sciences. Population data (spatial resolution: 100 m) for 2000, 2010, 2015, and 2018 were acquired from the WorldPop Project at https://www.worldpop.org/geodata/listing?id=16 (accessed on 7 May 2022). Boundaries of the YREB were taken from the National Fundamental Geography Information System webpage http://www.ngcc.cn/ngcc/ (accessed on 11 May 2022).

2.3. Applied Method

2.3.1. The Flowchart

Figure 2 presents the flowchart of this study. Firstly, we calculated the LCRPGR and analyzed the spatial patterns of its change over time in the prefecture-level cities and across the UAs in the YREB for 2000–2010, 2010–2015, and 2015–2018. We then used the STI method to understand the spatiotemporal dynamics of LCRPGR. Moreover, we determined the statistical relationship between LCR and PGR using Pearson’s correlation analysis. Finally, according to the results and policy, we concluded that the LUE should be monitored from the perspective of PGR versus LCR to achieve the sustainable development of the UAs.

2.3.2. Estimating the Indicator SDG 11.3.1

SDG 11.3.1 is primarily used to quantify the LUE as a consequence of spatially consumed land against the rate at which their populations grow [31,32]. The upper bound was defined as the LCRPGR value located close to the value of the top 2.5th percentile; the lower bound was defined as the LCRPGR value located close to the value of the bottom 2.5th percentile. We then classified the LCRPGR into five categories: LCRPGR ≤ −1.0, −1.0 < LCRPGR ≤ 0.0, 0.0 < LCRPGR ≤ 1.0, 1.0 < LCRPGR ≤ 2.0, and LCRPGR > 2.0 [4,8].

$$\mathrm{LCRPGR}=\frac{LCR}{PGR}$$

(1)

$$LCR=\frac{ln\left(\frac{Ur{b}_{t+n}}{Ur{b}_t}\right)}{y}$$

(2)

$$PGR=\frac{ln\left(\frac{Po{p}_{t+n}}{Po{p}_t}\right)}{y}$$

(3)

where Urb_t and Urb_t+n are the total areal extent of land consumed at the given spatial unit (e.g., prefecture-level city) for the initial year (t, 2000, then 2010, 2015) and final year (t + n, 2010, then 2015, 2018), respectively; Pop_t and Pop_t+n represent the total population at the given the corresponding spatial unit for the initial year (t) and final year (t + n), respectively; is the number of years between t and t + n; and ln refers to a natural logarithm.

Urb was derived from urban construction land based on the practical definition proposed by Song et al. [28] of the built-up boundary that maintains the core area of the city, and we removed independent pixels (within 5 pixels), small areas at the edge of the city, and satellite cities. This paper selects a multiphase land-use dataset, in which construction land is divided into urban construction land and rural residential land. Taking urban construction land as the scope of Urb, the Urb of the multiphase in-study area was extracted. Then, the extracted Urb was divided into 1 km × 1 km grids, and the percentage of the Urb was calculated in each grid in the YREB from 2000 to 2018.

2.3.3. Deriving Land-Use Efficiency

Land-use efficiency (LUE), defined as the rate of change in LCRPGR between the first time period (P1) and the second time period (P2), was derived using the following formula:

$$\mathrm{LUE}=\left(\frac{{\mathrm{LCRPGR}}_{P2}-{\mathrm{LCRPGR}}_{P1}}{{\mathrm{LCRPGR}}_{P1}}\right)\times 100$$

(4)

A positive value indicates that land-use efficiency decreases over time, while a negative value indicates an increase in LUE. We classified the LUE into seven categories: high increase (≤−100%), medium increase (−100% to −50%), low increase (−50% to 0%), no change (0%), low decrease (0% to 50%), medium decrease (50% to 100%), and high decrease (>100%) [4].

2.3.4. Spatiotemporal Analysis

We used the space–time interaction (STI) method given by Legendre et al. [33] to understand the spatial and temporal dynamics of LCRPGR. This method tests for space–time interactions in repeated data, where there are no replications at the level of individual sampling units. In STI, variables of time and space are coded using principal coordinates of neighbor matrices in a two-way analysis of variance (ANOVA) model [34]. The STI’s significant results (p < 0.05) show that LCRPGR’s spatial distribution has changed over time. In this study, we used the R package adespatial (version 0.3–8) to process STI, setting each STI test at 999 permutations [33,35].

2.3.5. Statistical Analysis

We used a correlation analysis (i.e., Pearson’s r) to examine the statistical relationship between LCR and PGR during the period 2000–2018 in CC, the MRYR, the YRD, and the YREB.

3. Results

3.1. Trends of Built-Up Expansion and Population Growth

On average, the YREB’s population has been increasing at a rate of 3.05 million people/year, while the YREB’s built-up area has been expanding at a speed of 928.83 km²/year. Specifically, the YREB’s population increased 2.1-fold from 54.44 million in 2000 to 112.38 million in 2018 (Figure 3a), and its built-up area increased 2.7-fold from 10,436.74 km² in 2000 to 28,084.58 km² in 2018 (Figure 3b).

CC’s population increased 1.8-fold from 8.12 million in 2000 to 14.26 million in 2018, and its built-up area increased 2.5-fold from 1101.66 km² in 2000 to 2788.74 km² in 2018. The MRYR’s population increased 1.8-fold from 11.48 million in 2000 to 20.12 million in 2018, and its built-up area increased 2.1-fold from 2242.76 km² in 2000 to 4685.7 km² in 2018. The YRD’s population increased 2.4-fold from 22.45 million in 2000 to 53.36 million in 2018, and its built-up has area increased 3.1-fold from 4218.09 km² in 2000 to 12,899.52 km² in 2018 (Figure 3).

In three UAs (i.e., CC, the MRYR, and the YRD), the YRD had the highest proportion of the YREB’s population and built-up area during 2000–2018 (Figure 3a,b). Both the population and the built-up area of the MRYR and CC have increased over the years. Regardless of population or built-up area, the percentage share of the YRD has always been the highest. During 2000–2018, the gaps in both population and built-up area between YREB and the three UAs did not narrow, but widened (Figure 3a,b).

3.2. Spatiotemporal LCRPGR Changes

At the UA level, CC had the highest land-use efficiency in the three periods, followed by the MRYR, YRD, and YREB (

Table 1. LCRPGR and LUE at the UA level.

Urban Agglomerations	LCRPGR			Change in LCRPGR (%)
	2000–2010 (P1)	2010–2015 (P2)	2015–2018 (P3)	LUEP1–P2	LUEP2–P3	LUEP1–P3
CC	1.77	−0.82	0.31	−146.32	−137.81	−82.49
MRYR	1.23	2.35	0.92	91.06	−60.85	−25.20
YRD	1.36	1.43	1.84	5.15	28.67	35.29
YREB	1.21	1.23	1.16	1.65	−5.69	−4.13

P1, P2, and P3 are the first time period (2000–2010), second time period (2010–2015), and third time period (2015–2018), respectively.

). A major difference was that the land-use efficiency of the YRD decreased over the three periods, whereas that of the MRYR and YREB first decreased and then increased (Table 1).

Moreover, in the prefecture-level cities, the LCRPGR increased (LCRPGR > 0) in 16 (100%), 13 (81%), and 11(69%) of the 16 prefecture-level cities across CC in 2000–2010, 2010–2015, and 2015–2018, respectively (Figure 4 and

Table 2. Statistics on the number prefecture-level cities of LCRPGR < 0 and LCRPGR > 0 for 2000–2010, 2010–2015, and 2015–2018 in CC, the MRYR, the YRD, and the YREB.

	CC		MRYR		YRD		YREB
	Mean	SD	Mean	SD	Mean	SD	Mean	SD
LCRPGR < 0	3 (17%)	2 (13%)	3 (11%)	2 (8%)	1 (3%)	1 (3%)	13 (17%)	7 (7%)
LCRPGR > 0	13 (83%)	2 (13%)	25 (89%)	2 (8%)	24 (97%)	1 (3%)	113 (83%)	7 (5%)
Number of Prefecture-Level Cities	16		28		25		126

). Specifically, the MRYR, YRD and YREB had the highest proportions in 25 ± 2 (89 ± 8%) of 28 prefecture-level cities, 24 ± 1 (97 ± 3%) of 25 prefecture-level cities, and 113 ± 7 (83 ± 5%) of 126 prefecture-level cities with increasing LCRPGR values, respectively (Figure 4 and Table 2).

In Figure 5, the spatiotemporal dynamics of the LCRPGR in all prefecture-level cities are shown. For CC (Figure 5a), an obvious initial decrease and subsequent increase was observed from 2000 to 2018. In contrast, a clear initial increase and subsequent decrease was observed across the MRYR (Figure 5b) during the same periods. For the YRD (Figure 5c) and YREB (Figure 5d), LCRPGR showed a weak growth trend and tended to stabilize over time, respectively. Results from the STI analysis (p < 0.05) indicate that this stabilization trend only happened in a few specific prefecture-level cities (Figure 5d). In reality, the spatial patterns of LCRPGR were affected by not only the spatial heterogeneity in the YREB (p < 0.05), but also the conditions of natural resources and the economic basis.

Overall, the spatiotemporal dynamics of the LCRPGR showed that the selected UAs experienced a fluctuation trend. Only trends in the YRD increased weakly, while trends in the YREB tended to stabilize from 2000 to 2018.

3.3. Relationship between LCR and PGR

A significant positive relationship (p < 0.001) was found between LCR and PGR in CC, the MRYR, the YRD, and the YREB (Figure 6). In the prefecture-level cities, LCR and PGR were also positively and significantly correlated over the entire study period (2000–2018) in the four UAs, namely, CC (N = 48, Pearson’s r = 0.425, p < 0.001), the MRYR (N = 84, Pearson’s r = 0.90, p < 0.001), the YRD (N = 75, Pearson’s r = 0.85, p < 0.001), and the YREB (N = 378, Pearson’s r = 0.83, p < 0.001) (Figure 6).

4. Discussion

According to UN-Habitat reports, cities are expanding spatially at a faster rate than population growth, which can cause problems and have an impact on city-related disasters, climate change, urban planning, and policies [32].

The indicator SDG 11.3.1 was designed to monitor LUE as urbanization threatens urban sustainable development. This study uses CLUDs data to extract information on built-up land, and combines population data to calculate and monitor the LCRPGR of the YREB and its three major UAs in China.

The world average LCRPGR value given by the UN SDG report 2017 for the period 2000–2015 was 1.28 [6,18]. Compared with the world average of 1.28 for LCRPGR, except for the LCRPGR of the YRD (2000–2018) and CC (2000–2010), the LCRPGR of CC, the MRYR, and the YREB was lower than 1.28 throughout the entire study period (2000–2018) (Table 1), indicating that LCRPGR in CC, the MRYR, and the YREB was lower than the world average.

The Big Earth Data in Support of the SDGs (2020) reported that the LCRGR of 433 cities in China increased from 1.33 during 1990–1995 to 2.15 during 2010–2015, and that since 2015 the rate of urban expansion has slowed down. Compared with the LCRPGR in China, except for the LCRPGR of the YRD (2000–2018) and CC (2000–2010), the LCRPGR of the YREB increased from 1.21 during the period 2000–2010 to 1.23 during the period 2010–2015, and then decreased to 1.16 during the period 2015–2018 (Table 1), indicating that the relationship between LCR and PGR in the YREB is relatively stable, but it belongs to the urban land expansion type. Unlike Opole, Ethiopia, and other urban agglomerations in China [20,21], most cities in the YREB have a positive LCRPGR, while CC had a negative LCRPGR during 2010–2015 due to negative population growth in surrounding small cities. Chengdu and Chongqing, as economically developed cities in the southwestern region, have obvious core–periphery effects on the surrounding small cities, leading to the phenomenon of population loss. The core–periphery effects are reflected in the migration of the central and western population to the eastern region in China, or the strong attraction to the population of surrounding cities, suburbs, and rural settlements [25]. The LCRPGR values of the YREB were 1.21, 1.23, and 1.16 in 2000–2010, 2010–2015, and 2015–2018, respectively, which were lower than the LCRPGR values of 2.1 and 1.9 in 1990–2000 and 2000–2015 for sampled cities in developed countries, respectively [16]. The value of LCRPGR in the MRYR during the period 2010–2015, and in the YRD during the period 2015–2018, slightly outperformed the global stratified sample city average LCRPFR in 1990–2000 and 2000–2015 (1.68 and 1.74, respectively), and surpassed China’s LCRPGR in 1990–2000 and 2000–2010 (1.69 and 1.78, respectively).

The UAs that changed most dramatically were CC and the MRYR, which decreased from 1.77 during the period 2000–2010 to −0.82 during the period 2010–2015, and then to 0.31 during the period 2015–2018 (Table 1). By contrast, the MRYR and CC increased from 1.23 during the period 2000–2010 to 2.35 during the period 2010–2015, and then to 0.92 during the period 2015–2018, indicating that the LCR and PGR in CC and the MRYR underwent significant changes. This could be due to two reasons: Firstly, the average LCRPGR values given by the UN SDG report 2017 and the Big Earth Data in SDGs (2020) were calculated based on a different data source as those of this paper. Secondly, UAs of different sizes and different urban expansion types are used to calculate the average LCRPGR within a geographical area, which mainly shows the average level of LUE, and does not pay attention to cities with severe urban land growth or severe urban population growth phenomena.

Theoretically, a reasonable LCRPGR value should be equal to or close to 1 [36], indicating a balance between urban land-use consumption and population growth in UAs. This study shows that there were two types of change trends during the measurement periods 2000–2010, 2010–2015, and 2015–2018 (Table 1): One is that the LCRPGR value for the MRYR and YREB for the period 2015–2018 is closer to the ideal value of 1 than the values for the periods 2000–2010 and 2010–2015, meaning that the urban land-use efficiency was improved in the MRYR and YREB. The second is that the LCRPGR value in CC and the YRD for the period 2000–2010 is closer to the ideal value of 1 than the values for the periods 2010–2015 and 2015–2018, which means that the urban land-use efficiency declined in CC and the YRD. Therefore, CC and the YRD are facing the problem of inefficient use of urban land, either with relatively larger or smaller urban land consumption compared with the PGR [7]. Due to differences in factors such as socioeconomics, regional locations, and policy orientations, there are differences in the expansion intensity of different cities. For example, the growth of the built-up area in CC is mainly reflected in the provincial capital cities. Because the provincial capital cities have a strong attraction to the whole province, the built-up area of the provincial capital cities has grown rapidly. In terms of policy, the dual land system has led to the formation of two markets—for agricultural land and non-agricultural land—in China’s land market. The low cost of land acquisition and rapid industrial development have transformed a large number of agricultural land markets into non-agricultural land markets, driving the growth of urban land consumption rates. Different land policies in different cities also lead to differences in land-use efficiency.

It is worth noting that our calculated results have many outliers of LCRPGR. These outliers of LCRPGR are generated mostly by small changes in urban population growth over time [6]. If these outliers of LCRPGR are used to evaluate the LUE, it will inevitably reduce the reliability of the results [6]. Thus, relevant scholars need to revise the SDG 11.3.1 indicator to conceive a more comprehensive one [4].

On the scale of UAs, a significant positive relationship (p < 0.001) was found between LCR and PGR (Figure 6), which is inconsistent with the findings of Estoque et al. [4], who found a significant positive relationship between LCR and PGR in only some regions [4]. One possible reason for this difference might be related to land-use policies. China’s land-centered expansion of built-up areas is influenced by regulatory land control from land governance and land-use planning [24]. Therefore, the land-use policies of the country’s establishment can guide land consumption for the local government, and can provide instruction to properly arrange newly increased urban land to maximize the benefits of regional integration [24]. This finding might have important implications in spatial simulation and spatial prediction models of future urban expansion [4], especially at the UA level.

There are several papers in the literature on LCRPGR monitoring at the global [4,5], regional [6,7], national [8], and local [9,10,11,12,13,14,15] levels. As highlighted by Li et al. [6], the procedures used to assess the indicator SDG 11.3.1 differ from city to city. Therefore, it is very difficult to compare their results. We suggest that the administrative boundaries of the LCR and PGR calculations are the same [4], and that those data used should be highly spatiotemporally consistent, so as to ensure that the results of the monitored land-use efficiency are statistically comparable with one another.

5. Conclusions

We monitored the LCRPGR in the YREB and its three major UAs (i.e., CC, the MRYR, and the YRD) for 2000–2018 and its three sub-periods (i.e., 2000–2010, 2010–2015, and 2015–2018), mapping spatiotemporal LCRPGR changes and relationships between LCR and PGR in the prefecture-level cities using the indicator SDG 11.3.1, STI, and Pearson’s method, and analyzing the spatial pattern of LCRPGR changes over time at the UA level. Three major findings are supported: (1) On average, the YREB’s population has been growing at a rate of 3.05 million people/year, and the YREB’s built-up area has been expanding at a rate of 928.83 km²/year. (2) The gaps in both population and built-up area between the YREB and the three UAs has not narrowed, but widened. (3) At the UA level, the spatial and temporal dynamics of the LCRPGR indicate that the majority of the selected UAs showed a fluctuation trend. Only trends in the YRD increased weakly, while trends in the YREB tended to stabilize from 2000 to 2018. (4) A significant positive relationship (p < 0.001) was found between LCR and PGR in CC, the MRYR, the YRD, and the YREB. Our findings can help to monitor LUE in UAs, and to provide a case study in monitoring the SDG 11.3.1 for UAs.