Sustainable Development Goal 6 Assessment and Attribution Analysis of Underdeveloped Small Regions Using Integrated Multisource Data

Abstract

Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions hold greater operational significance for achieving the 2030 Agenda for Sustainable Development from the bottom up; thus, urgent attention should be given to data deficiencies and inadequate analyses related to SDG impact attribution. This study, conducted in the National Innovative Demonstration Zone for Sustainable Development of Lincang City, investigates multisource data sources such as integrated statistics, survey data, and remote sensing data to analyze the progress and status of SDG 6 achievement from 2015–2020, and employs the LMDI decomposition model to identify influential factors. The assessment results demonstrate that the SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020. The SDG 6 indicators and SDG 6 composite index have significant spatial heterogeneity. The water resources indexes in wealthy countries are high, the water environment and water ecology indexes in developing countries are comparatively high, and the SDG 6 composite index is high in undeveloped counties. Technological and economic advances are the main positive drivers impacting the SDG 6 composite index, and the relative contributions of technology, economy, structure, and population are 61.84%, 54.16%, −4.03%, and −11.96%, respectively. This study shows that integrated multisource data can compensate for the lack of small-scale regional statistical data when quantitative and comprehensive multi-indicator evaluations of the SDGs are conducted. And, policies related to SDG 6.1.1, SDG 6.2.1, and SDG 6.3.1 can be a priority for implementation in undeveloped regions with limited funding.

1. Introduction

SDG 6 (ensure availability and sustainable management of water and sanitation for all) is central to the 17 Sustainable Development Goals (SDGs). It is both directly and indirectly linked to 13 other SDGs. SDG 6 lays the foundations for improving human well-being, supporting economic and social progress, and preserving healthy ecosystems. Furthermore, it is a requirement for implementing the 2030 Agenda for Sustainable Development [1,2,3,4,5,6,7]. However, according to the United Nations Sustainable Development Goals Report (2022), at the current rate of progress, 1.6 billion people will still lack access to safe drinking water, 2.8 billion will lack access to safe sanitation facilities, and 1.9 billion will be deprived of basic hand hygiene by 2030. The rate of improvement must be quadrupled to meet SDG 6. To “let no one fall behind”, this study evaluates the status of SDG 6 implementation during 2015–2020, evaluates the effect of water-related policy implementation in certain regions from 2015–2020, and provides important references for the improvement of policies related to SDG 6 and the 2030 Agenda for Sustainable Development.

To accelerate SDG 6 implementation, the UN designed and launched the “SDG 6 Global Acceleration Framework”, which includes five cross-cutting domains: finance, data and information, capacity development, innovation, and governance. Regarding data and information, the SDG 6 Progress Summary Report (2021) states that most of the 193 UN member states have collected two-thirds of the necessary SDG 6 monitoring data, with 24 countries still having collected less than half of the required data; data are especially limited in small-scale regions below the country level. Existing SDG 6 studies at home and abroad are largely focused on large scales, such as global, regional, and national scales [8,9], and a lack of statistical data is a major challenge for poorly developed small-scale regional SDG 6 research [10]. Small-scale regions such as cities and counties are the specific implementation units of the SDGs, and are used to assess SDG progress to ensure local implementation of the nation’s sustainable development policies [11,12].

According to the classification of monitoring evaluation methods and the data status of global sustainable development indicators in 2022, four SDG indicators, SDG 6.1.1, SDG 6.2.1, SDG 6.3.1, and SDG 6.3.2, have methods but no valid data (Tier II) at the national scale [13]. Currently, the SDG 6 assessment focuses primarily on public water availability (SDG 6.1.1) and environmental sanitation facilities (SDG 6.2.1), with data for both indicators derived primarily from small-scale survey data, with long survey cycles, high access costs, and poor time continuity [14,15,16,17]. Single data sources frequently fail to meet research needs in multi-indicator comprehensive assessment studies, particularly at subnational spatial scales. Multisource data, such as integrated statistical databases and remote sensing data, provide an effective measure to address data shortages in SDG assessments and can contribute to the implementation of local SDG 6 and the 2030 Agenda for Sustainable Development [18,19].

The progress toward achieving sustainable development goals varies by income level and location [20]. Furthermore, the identification of the impact elements of SDG 6 can provide a theoretical basis for the rational development of regional water resources and the policy designs for water-related ecological environment conservation. SDG 6 comprises eight targets and eleven indicators, which can be divided into four groups based on their relevance to water: water resources (SDG 6.1, 6.4), water environment (SDG 6.2, 6.3), water ecology (SDG 6.6), and water management (SDG 6.5, 6.a, 6.b). The majority of studies have focused on identifying the influencing variables of a single dimension, although the driving factors for each dimension vary. The main driving factors affecting the three dimensions of water resources, water environment, and water ecology are economic scale, population size, technology level, and industrial structure, and their primary data sources are statistical data [21,22,23,24,25,26,27,28]. Water demand and wastewater discharge rise along with economic and population growth, whereas lower water use and wastewater discharge typically result in technological advances [21,22,23,24,25,26,27]. There are fluctuations in the driving direction of industrial structures, and their influence on industrial wastewater discharge can change from supporting to suppressing [24].

Decomposition methods for influencing factor research include three basic decomposition methods: the index decomposition method, structural decomposition method, and production theory decomposition method. The logarithmic mean Divisia index (LMDI) model of the index decomposition method solves the problem of the residual term and zero value. The extended Kaya identity and the LMDI model are widely used in the analysis of influencing factors, which are often divided into four effects, intensity, structure, economy, and population, and widely used in many fields, such as carbon emissions, water resource utilization, industry employment population, and construction area. This study focuses on analyzing the quantitative changes in the SDG 6 composite index, which comprises three dimensions, of water resources, water environment, and water ecology. The addition decomposition can provide overall change and decomposition results, which mostly match with quantity indicators, and LMDI-Ⅰ can provide the properties of aggregation consistency and perfect decomposition over subdimensions; thus, model 1 is the preferred model based on Ang’s methodological recommendations.

This work incorporates multisource data, including statistical data, survey data, and remote sensing data, to address the data scarcity regarding SDG 6 and to provide an attribution analysis. It undertakes a thorough evaluation of SDG 6 using multiple indicators in Lincang City, a National Innovative Demonstration Zone for Sustainable Development, from 2015 to 2020. Using the LMDI model, this study identifies the elements impacting SDG 6 realization and provides appropriate recommendations for the rational development and protection of water resources. The aim is to provide a reference for formulating SDG 6 implementation policies in Lincang City and implementation guidelines in other undeveloped mountainous regions, such as Southwest China and Southeast Asia.

2. Materials and Methods

2.1. Study Area

Since 2018, the Chinese government has established 11 “National Innovative Demonstration Zones for Sustainable Development” in three batches to comprehensively promote the implementation of the United Nations’ 2030 Agenda for Sustainable Development. Lincang City was approved with the theme of “innovation-driven development in underdeveloped border areas with multiple ethnic groups”. Lincang City, located between the Lancangjiang and Nujiang areas, is an important biodiversity conservation zone on Yunnan Province’s southwest border, between 98°40′~100°32′E and 23°05′~25°03′N (Figure 1). The city covers a total land area of 23,620 km², with mountains accounting for 97.5% and forests accounting for 70.20%. Water resources are abundant, with an average total water resource of 13.02 billion m³ during the last ten years and per capita water resources of 5164 m³, which is approximately twice that of the country’s per-person water resources; however, water facility engineering measures, which address water scarcity, are poor, with only 7.7% of water resources developed and utilized. At the same time, Lincang City has a wealth of water energy resources, and hydroelectric power generation in China has provided abundant clean energy for China’s “Western Power East” and “Yun power outsourcing”. However, this power advantage has not been able to completely drive local economic development. The city has a subtropical low-latitude plateau mountainous monsoon climate with plentiful sunshine and ample precipitation. Its four seasons, all of which resemble spring, are ideal for the development of agriculture. Primary industry accounted for 29.93% of its GDP in 2021, much higher than the average levels in Yunnan Province and China (14.3% and 7.3%, respectively). However, the water-use efficiency of agriculture is relatively low, and agricultural water use accounts for 85.17% of all available water resource use and severely competes with water utilized for the environment (1.48%). Due to the mountainous terrain, a poor and dispersed population, and the lagging transportation and water conservation infrastructure, the resource advantages of the region have not been fully translated into economic advantages; economic development is still significantly lagging.

2.2. Data Sources

Among the seven SDG 6 indicators considered, six indicators of SDG 6, SDG 6.1.1, SDG 6.3.1, SDG 6.3.2, SDG 6.4.1, SDG 6.4.2, and SDG 6.6.1 have directly adopted SDG 6 indicators from the 2030 Agenda, whereas SDG 6.2.1 is a localized indicator (

Table 1. List of indicators for the sustainable development index of water resource development and protection.

Goal	Targets	Indicators	Indicator Meaning	Indicator Source	Indicator Direction
SDG 6 Ensure availability and sustainable management of water and sanitation for all	SDG 6.1	SDG 6.1.1	Proportion of population using safely managed drinking water services	A	Positive
	SDG 6.2	SDG 6.2.1	Density of public toilets in urban areas	L	Positive
	SDG 6.3	SDG 6.3.1	Proportion of domestic and industrial wastewater flows safely treated	A	Positive
		SDG 6.3.2	Proportion of bodies of water with good ambient water quality	A	Positive
	SDG 6.4	SDG 6.4.1	Change in water-use efficiency over time	A	Positive
		SDG 6.4.2	Level of water stress	A	Negative
	SDG 6.6	SDG 6.6.1	Change in the extent of water-related ecosystems over time	A	Positive

Note: A is direct adoption of “the 2030 Agenda for Sustainable Development”, L is localized indicators.

). Public toilets are a key component of urban environmental sanitation facilities, as well as being important characteristics of the region’s ecological civilization-building, so SDG 6.2.1 was replaced with the goal of achieving a certain density of public toilets in urban areas. According to previous studies, the data sources of the 7 indicators include statistics, survey data, remote sensing data, monitoring data, web data, and other multisource data, and the data involved in this study include statistical data, survey data, and remote sensing data from three data sources (Figure 2). The time range is 2015–2020, and the spatial scope is Lincang City and 8 counties under its jurisdiction. The statistics are primarily drawn from the Lincang Statistical Yearbook, the Report on the State of the Environment Statement, and the Lincang Water Resources Bulletin; the survey data are the number of public toilets; and the remote sensing data are drawn from the remote sensing monitoring dataset of land use and land cover in China. Additionally, influencing parameters, such as the total resident population, water-use structure, per capita GDP, and water-use efficiency of the three industries were obtained from the Lincang Statistical Yearbook (2015–2020) and the Lincang Water Resources Bulletin (2015–2020). Specific data can be found in Table S1.

2.3. Methods

This study used the SDG Indicator Metadata and the Sustainable Development Solutions Network (SDSN) from the United Nations to measure the seven indicators of SDG 6 considered and the sustainable development index of water resources development and protection (SDG 6 composite index) based on multisource data for the city and the eight counties. It also analyzed the progress and trends of SDG 6 indicators and the SDG 6 composite index. Combined with the 2020 expected value for each indicator, the status of SDG 6 implementation in the region was assessed. The LMDI approach was used to analyze the causes of the effect on the SDG 6 composite index, and specific suggestions for the sustainable development of regional water resources are offered on this basis (Figure 3). The results were visualized using Arcmap 10.6 and Origin 2018, and the following is the specific research approach used in this article:

2.3.1. Calculation of the Sustainable Development Index

This study used the same approach as that used for the United Nations SDG Indicator Metadata (Method S2), which measured seven SDG 6 indicators for the city and counties. According to the SDSN, sustainable development goals have holistic and indivisible characteristics, and each goal is equally important and should be given a fixed, consistent weight. The equal weighting method used for SDG 6’s five targets was used for Lincang City to determine the region’s SDG 6 comprehensive index. To avoid deviations due to unit differences and inconsistencies in the SDG 6 indicators, the indicator data were first classified with extreme differences (Equations (1) and (2) [29]. Then, the different attributes were divided into two categories, promoting regional water sustainability as a positive indicator and inhibiting it as a negative indicator. Only SDG 6.4.2 is a negative indicator (Table 1). All indications are converted into a dimensionless scale ranging from 0 to 1, with 0 representing poor performance and 1 representing outstanding performance. SDGs 6.1.1, 6.3.1, and 6.3.2 were standardized with a minimum value of 0 and a maximum value of 100%. The maximum and minimum values for the other indicators are the extreme data values over the research period.

$$x_n=\frac{x-\mathit{min}(x)}{\mathit{max}(x)-\mathit{min}(x)}\left(positiveindicator\right)$$

(1)

$$x_n=\frac{\mathit{max}(x)-x}{\mathit{max}(x)-\mathit{min}(x)}\left(negativeindicator\right)$$

(2)

2.3.2. Calculation of the Sustainable Development Index

The “SDG Index and Dashboards Report”, “China’s National Plan on Implementation of the 2030 Agenda for Sustainable Development” (National Plan of China), and the average SDG levels in China and Yunnan Province were used as the 2020 threshold of SDG 6 indicators when quantifying the development progress of SDG 6 indicators in Lincang City and the eight counties. The development progress was divided into four categories: achieved or expected to achieve (I), made progress but needs to be strengthened (Ⅱ), no progress or negative progress (Ⅲ), and no measurement criteria (Ⅳ). For indicators that have already reached 100% development, their progress is shown as achieved (I). This study used two indicators of poor performance under SDG 6 for Lincang and the eight counties to evaluate the development progress of SDG 6 based on the Sustainable Development Report released by the SDSN.

2.3.3. Influencing Factor Identification of the SDG 6 Composite Index

The extended Kaya identity and LMDI approach were used to analyze the factors influencing the SDG 6 composite index. Based on the Divisia index, the LMDI model is divided into eight models depending on the indicator polymerization (quantity and intensity indicators), the decomposition process (addition and multiplication decomposition), and the weight formula (LMDI-I, LMDI-II) [30]. The quantity indicators measure the absolute level of indicator change, while the intensity indicators measure the efficiency of indicator change; furthermore, the breakdown of quantitative indicators produces more information and is more extensively employed. The addition decomposition analyzes the change in the difference of the indicators, giving the overall change and the decomposition results, whereas the multiplicative decomposition decomposes the change in the ratio of the indicators, giving the aggregated change and the decomposition results. Furthermore, the findings of the additive and multiplicative decompositions can be converted and used interchangeably, with additive decompositions being more appropriate for use with quantitative indicators and multiplicative decompositions being more appropriate for use with intensity indicators. The LMDI-I and LMDI-II methods produce nearly identical results, with the LMDI-I formula being more widely used due to its simplicity and the properties of aggregation consistency and perfect decomposition at the subcategory level.

The additively decomposed LMDI-I is the preferred model for the SDG 6 composite index, which is a quantitative indicator. According to the factors influencing water resources, water environment, and water ecology, combined with the resource endowment and environment of Lincang City, the water-use structure, resident population, water-use efficiency of the three industries, and per capita GDP were selected to represent the structure, population, technology, and economic effects factors.

Kaya proposed the Kaya identity in 1989 to examine the degree to which population, economy, and policy influence CO₂ emissions. Scholars later enlarged the influencing elements to include several additional factors. Kaya’s equation in its extended form is as follows:

$$R=\sum _i\frac{W_i}{TW}\times \frac{TW}{GDP}\times \frac{GDP}{P}\times P=\sum _i{s}_i{e}_{i}pg\left(i=1,2,3\right)$$

(3)

The above formula was used to construct the relationship between the SDG 6 composite index and the water-use structure, water-use efficiency, economic level, and population. In the above formula, $R$ is the SDG 6 composite index; $W_i$ is the water resource utilization of the three industries; $TW$ is the total water resource utilization; $GDP$ is the regional gross product; $P$ is the resident population; s, e, p, g are the structure, technology, population, and economic effects factors, respectively; and $i$ is the type of industry, which includes primary, secondary, and tertiary industries.

An LMDI decomposition model of the SDG 6 composite index was constructed based on the extended Kaya identity, with 2015 as the base year ($t_0$) and 2020 as the final year ($t_1$). $∆R$ represents the change values of the SDG 6 composite index; $R_{t_0}$ and $R_{t_1}$ are the SDG 6 composite index in 2015 and 2020, respectively; ${∆R}_s$, ${∆R}_e$, ${∆R}_p$, and ${∆R}_g$ are the degree of structural, technological, population-based, and economic influence on the SDG 6 composite index;$s_{i{t}_0}$ and $s_{i{t}_1}$, $e_{i{t}_0}$ and $e_{i{t}_1}$, $p_{t_0}$ and $p_{t_1}$, $g_{t_0}$ and $g_{t_1}$ represent the values of structure, technology, population, and economy in 2015 and 2020; and $i$ is the type of industry, which includes primary, secondary, and tertiary industries.

$$∆R=R_t-R_0={∆R}_s+{∆R}_e+{∆R}_p+{∆R}_g$$

(4)

$${∆R}_s=\sum _i\frac{R_{t_1}-R_{t_0}}{ln{R}_{t_1}-ln{R}_{t_0}}ln\frac{s_{i{t}_1}}{s_{i{t}_0}}\left(i=1,2,3\right)$$

(5)

$${∆R}_e=\sum _i\frac{R_{t_1}-R_{t_0}}{ln{R}_{t_1}-ln{R}_{t_0}}ln\frac{e_{i{t}_1}}{e_{i{t}_0}}\left(i=1,2,3\right)$$

(6)

$${∆R}_p=\sum _i\frac{R_{t_1}-R_{t_0}}{ln{R}_{t_1}-ln{R}_{t_0}}ln\frac{p_{t_1}}{p_{t_0}}$$

(7)

$${∆R}_g=\sum _i\frac{R_{t_1}-R_{t_0}}{ln{R}_{t_1}-ln{R}_{t_0}}ln\frac{g_{t_1}}{g_{t_0}}$$

(8)

3. Results

3.1. Temporal and Spatial Variation Characteristics of SDG 6 Indicators

From 2015 to 2020, the SDG 6 indicators in Lincang City and its counties all exhibited robust increases, while the overall progress of the seven indicators in Lincang City was inconsistent, with clear spatial variations between counties (Figure 4). Lincang City and the three counties of Gengma County, Shuangjiang County, and Yongde County have all achieved or expected to achieve SDG 6 (I), whereas Linxiang County, Fengqing County, Cangyuan County, Zhenkang County, and Yun County have made progress but need to be strengthened (II).

Table 2. Assessment of the realization status of the SDG 6 indicators in Lincang City.

Region		SDG 6.1.1	SDG 6.2.1	SDG 6.3.1	SDG 6.3.2	SDG 6.4.1	SDG 6.4.2	SDG 6.6.1	SDG 6
City	Lincang	I	I	I	I	Ⅱ	I	Ⅳ	I
County	Linxiang	Ⅱ	I	I	I	Ⅱ	I	Ⅳ	Ⅱ
	Fengqing	I	Ⅱ	I	I	Ⅱ	I	Ⅳ	Ⅱ
	Gengma	I	I	I	I	Ⅱ	I	Ⅳ	I
	Cangyuan	I	I	I	I	Ⅲ	I	Ⅳ	Ⅱ
	Shuangjiang	I	I	I	I	Ⅱ	I	Ⅳ	I
	Yun	I	Ⅱ	I	I	Ⅱ	I	Ⅳ	Ⅱ
	Yongde	I	I	I	I	Ⅱ	I	Ⅳ	I
	Zhenkang	Ⅲ	I	I	I	Ⅱ	I	Ⅳ	Ⅱ

Note: I–Ⅳ refer to the development progress categories, such as “achieved or expected to achieve” (I), “made progress but needs to be Strengthened” (Ⅱ), “no progress or negative progress” (Ⅲ), and “no measurement criteria” (Ⅳ).

shows the SDG 6 realization status of Lincang City and its counties. SDG 6 is on track, which could be attributable to the Lincang Municipal Government’s focus on water resources. Several policies influencing water conservation, water quality, and human drinking projects were developed from 2015 to 2020. Each SDG 6 indicator’s status and trends are displayed below.

3.1.1. SDG 6.1.1 (Proportion of Population Using Safely Managed Drinking Water Services)

Significant progress was made in SDG 6.1.1 by Lincang City, which increased from 82.44% in 2015 to 95.54% in 2020, an increase of 15.89%. The penetration rate of the public water supply reached 100% in Fengqing County and Gengma County, but it was only 81.4% in Zhenkang County. Except for Zhenkang and Linxiang counties, the penetration rate of public water delivery in other counties exceeded the recommended level of the “SDG Index and Dashboards Report” of more than 95%, and only Zhenkang County showed a result lower than the “National Plan of China” expected value of more than 85%. Most counties have achieved SDG 6.1.1 (I), whereas Linxiang County has made progress, but their efforts need to be strengthened (Ⅱ), and Zhenkang County shows a downwards trend, and has the position of negative progress (Ⅲ).

Lincang City’s full compliance with SDG 6.1.1 may be related to the municipal government having emphasized the construction of drinking water projects in its relevant policies. At the end of 2020, RMB 842 million had been invested to consolidate and improve the water supply guarantee level for 1.65 million rural residents, and over 24.40 thousand rural drinking water projects had been built. Zhenkang County’s relatively slow rate of SDG 6.1.1 may be attributed to the rapid expansion in urbanization, which climbed from 30,100 in 2019 to 34,800 in 2020.

3.1.2. SDG 6.2.1 (Density of Public Toilets in Urban Areas)

SDG 6.2.1 had a clear upward trend, with less than 1 place/km² in 2015 and more than 4 places/km² in 2020 in Lincang City. Except in Yun County and Fengqing County, the other counties had more than 3 places/km² in 2020. Except for Yun and Fengqing counties, the number of public toilets also increased to more than 4 places/km² in 2020, in line with the “code for planning of urban environmental sanitation facilities” (GB/T50337–2018), which sets a density of 3~5 places/km²; most counties have achieved SDG 6.2.1 (I), whereas Yun and Fengqing counties have made progress, but their efforts need to be strengthened (II).

Further investigation revealed that the increase in the number of environmental sanitation facilities in Lincang City is related to the Lincang urban public toilet renovation and construction plan (2018–2020), the “7 special actions” of patriotic health, other regulations that have resulted in a large increase in the number of public toilets, and the fact that RMB 80 million had been invested in construction.

3.1.3. SDG 6.3.1 (Proportion of Domestic and Industrial Wastewater Flows Safely Treated)

SDG 6.3.1 increased from 79.23% in 2015 to 99.17% in 2020 in Lincang City, an increase of 25.17%. The counties within Lincang City have seen an increase over time, with Fengqing County showing the largest growth, with SDG 6.3.1 going from 64.11% in 2015 to 100% in 2020, an increase of 55.98%. By 2020, three counties, Cangyuan, Gengma, and Fengqing, had achieved a 100% wastewater treatment rate, and the other five counties had achieved a wastewater treatment rate of more than 97.50%, all of which were higher than the 50% set by the “SDGs index and indicator board”; all have achieved SDG 6.3.1 (I).

The improvement in the wastewater treatment rate is closely related to the government’s infrastructure construction. Lincang City has implemented a policy of the human settlements action plan (2016–2020), investing RMB 0.85 billion to accelerate the construction of the urban sewage network, and the actual treatment capacity of urban sewage in 2020 was approximately 87,500 tons/day, which basically matches the current requirements.

3.1.4. SDG 6.3.2 (Proportion of Bodies of Water with Good Ambient Water Quality)

Lincang City’s assessment of the county-level and above centralized drinking water sources with good water quality rates, water functional area, and water quality standards remain at 100%; thus, SDG 6.3.2 has been achieved (I). According to the findings, Lincang City strictly followed the technical guidelines for delineating source water protection areas, implementing numerous projects, including water source projects, water quality monitoring projects, river regulation projects, and the “River Manager & District Manager” system, costing a total of RMB 1.26 billion; thus, the water quality of all drinking water sources and water functional zones was appropriately guaranteed.

3.1.5. SDG 6.4.1 (Change in Water-Use Efficiency over Time)

Lincang City’s water-use efficiency increased 19.18%, from 40.58 RMB/m³ in 2015 to 48.37 RMB/m³ in 2020; it ranges from below 10 RMB/m³ in sectors that depend on agriculture to over 170 RMB/m³ in industrial or services sectors. While it is still significantly lower than the national level or worldwide level, national water-use efficiency increased from 116.72 RMB/m³ in 2015 to 161.73 RMB/m³ in 2020; worldwide water-use efficiency increased from 17.4 USD/m³ in 2015 to 18.9 USD/m³ in 2020.

The differences among county-level administrative districts are significant. Except for Cangyuan County, the water-use efficiency in the counties showed increases. Linxiang County had the fastest improvement in water-use efficiency, which rose 50.18% to 113.67 RMB/m³ in 2020, more than double the average water usage efficiency of Lincang City. In contrast, Cangyuan County’s water usage efficiency lessened 8.21%, reaching 29.12 RMB/m³ in 2020. The progress of regional SDG 6.4.1 is the most lagging of SDG 6 indicators. Although the water-use efficiency of Lincang City and its counties is improving, it is still below the global or national average. Regarding SDG 6.4.1, Lincang City and all counties except Cangyuan are positioned to make progress, but efforts need to be strengthened (Ⅱ), and Cangyuan has a position of negative progress (Ⅲ).

The achievement of SDG 6.4.1 may be linked to policy and geographic advantages. Lincang City has formulated and implemented the implementation plan for the strictest water resources management system in Lincang City, which has established the “three red lines” control objectives of water resources management at the city, county, and district levels for 2015, 2020, and 2030, including control of total water consumption, control of water-use efficiency, and control of water quality standards in water functional areas. While water-use efficiency has progressively improved, there is still room for improvement in the region’s development of water-saving technology and water usage efficiency. Furthermore, Linxiang County is the municipal government’s seat, and its regional production factor concentration degree and technological level are higher than those in other counties, resulting in comparatively high water-use efficiency.

3.1.6. SDG 6.4.2 (Level of Water Stress)

Lincang City’s SDG 6.4.2 shows a fluctuating upward trend, going from 7.60% in 2015 to 9.13% in 2019 and then declining to 6.37% in 2020. The degree of water stress in other counties fluctuated upward, except in Zhenkang County, where it fluctuated downward, and in Gengma County and Cangyuan County, where it changed minimally. In terms of achievement status, Lincang City and its counties are below the recommended 25% of the SDG Index and indicator board criteria, achieving SDG 6.4.2 (I). Lincang City is rich in water resources but may waste water resources by relying on draft water. Although the region’s current water stress level is acceptable, it is increasing. It is vital to provide government support and advice, strengthen water resources management, and develop water resources responsibly.

3.1.7. SDG 6.6.1 (Change in the Extent of Water-Related Ecosystems over Time)

SDG 6.6.1 in Lincang City shows a fluctuating downward trend, falling from 109.48% in 2010–2015 to 18.87% in 2018–2020. Fengqing County, Yongde County, and Shuangjiang County all show a significant downward trend, similar to Lincang City. The largest reduction is in Fengqing County, where SDG 6.6.1 fell from 1182.34% in 2010–2015 to 2.88% in 2018–2020. Zhenkang County, Cangyuan County, Yun County, and Linxiang County show the opposite trend, and the fastest growth was recorded in Zhenkang County, where SDG 6.6.1 went from 9.10% in 2010–2015 to 196.02% in 2018–2020. Gengma County shows a more stable change.

Water-related ecosystems cover a wide range of counties, with Fengqing County having the highest proportion and Cangyuan County having the lowest, accounting for 2.39% and 0.10% of the total land use area, respectively. The difference was mainly caused by the construction of regional water facilities projects, and RMB 12.60 billion was spent on building and maintaining regional water conservancy projects between 2015 and 2020, with Fengqing County receiving the most funding. Fengqing County has the highest proportion of water-related ecosystems, while the Yunnan Xiaowan Hydropower Station is located at the junction of Nanjian County in Dali City and Fengqing County in Lincang City. This project was completed in 2015; thus, Fengqing County’s water ecology index in 2015 was significantly higher than that of other counties. Due to the absence of reference criteria (IV) for SDG 6.6.1, the realization status assessment was not carried out.

3.2. The Assessment of the SDG 6 Composite Index and Three Dimensions

Lincang City and each county’s sustainable state of water resource development and protection were examined in three dimensions: water resources (SDG 6.1, 6.4), water environment (SDG 6.2, 6.3), and water ecology (SDG 6.6), using an equal weighting method for each target of 0.2 (Figure 5).

The water resources index is on the rise in Lincang, growing from 0.26 in 2015 to 0.29 in 2020. However, there are considerable variances between counties. Linxiang County and Gengma County have higher water resources indexes, and these are lower in Fengqing and Shuangjiang counties for 2019 to 2020, which may be due to the economic scale and industrial structure. When the industrial structure is reasonable, the water resources index is relatively high in more developed regions and comparatively low in less developed regions. Within Lincang City, the following counties are ranked according to GDP per capita: Linxiang, Gengma, Fengqing, Shuangjiang, Yun, Zhenkang, Cangyuan, and Yongde. Further research reveals that Linxiang County has the greatest GDP per capita and a 14.39% share in the first industry in 2020. Fengqing County and Shuangjiang County are among the highest-ranking counties in the region in terms of GDP per capita, and these two counties produce the majority of the region’s food (37.67% and 28.13% of the primary industry output value in 2020, respectively). Because agriculture consumes a large amount of water, there is much pressure on these two counties to reduce water use.

Lincang City and all its counties’ water environment indexes likewise demonstrated an increasing trend, rising from 0.19 in 2015 to 0.31 in 2020. The water environment index for the eight counties is highest in Zhenkang County, followed by Shuangjiang and Yongde Counties, and lower in Fengqing and Gengma Counties. Further investigation shows that Zhenkang County’s economic development is considerably lagging behind that of Fengqing and Gengma Counties. However, the number of people who can be served by public toilets is quite large in Zhenkang County, which has the smallest built-up area and the lowest total population in the urban region, preventing the idleness of public facilities. Fengqing County, which is second only to Linxiang County in terms of built-up area and total urban population, has a low density of public toilets (less than 3/km²) and a comparatively high cost for supplying the essential public facilities. Gengma County had a comparatively low percentage of wastewater treated, 91.43%, in 2019. This finding demonstrates that the scale of the economy, population size, and wastewater treatment capacity all have an impact on the water environment.

The general trend of the water ecology index is dropping, falling from 0.02 in 2015 to 0.01 in 2020. Among the eight counties, Fengqing County had the highest water ecological index, 0.2, in 2015, whereas Zhenkang County had the highest water ecological index, 0.03, in 2020. The area of water-related ecosystems in Lincang City is growing, but the rate of growth is slowing. The region’s natural resource endowment and water facility engineering are related to the scale of the water-related ecosystem area and its pace of change. The bulletin of the main data of the third national land survey of Lincang City states that the city’s various water-related ecosystems, Yun County, Yongde County, Zhenkang County, and Gengma County, account for 78.52% of the city’s wetlands, while Fengqing County, Yun County, and Shuangjiang County account for 62.07% of the total area of waters and water conservancy facilities.

The SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020, with an overall noticeable positive trend. The city’s general level of sustainable development of water resources is uneven, with disparities between counties. Zhenkang County has the highest SDG 6 composite index, followed by Yongde County, while Fengqing County has the lowest. Further investigation revealed that economic scale, industrial structure, and technological level all influence the level of sustainable development of water resources, and that the economic development level of the county moves in the opposite direction of the SDG 6 composite index. In particular, Zhenkang County and Yongde County have a relatively lower economic development level; Zhenkang has a higher water resources score, the highest water environment and water ecology index, with its SDG 6 composite index growing from 0.47 in 2015 to 0.72 in 2020; and Yongde has a higher water resources index and water environment index and the lowest water ecology index, with its SDG 6 composite index rising from 0.45 in 2015 to 0.64 in 2020. Meanwhile, Fengqing County had a higher GDP, ranking second only to Linxiang County, and had the highest SDG 6 composite index in 2015. And, the primary industry used more than 85% of the water in this county, which is the province’s highland special agricultural demonstration county. The rate of change in the water-related ecosystem area is decreasing, with the SDG 6 composite index falling from 0.60 in 2015 to 0.54 in 2020.

3.3. Influencing Factor Identification of the SDG 6 Composite Index

The study focuses on four major drivers, economy, technology, population, and structure, to better understand the variables affecting the sustainable level of water resources in Lincang. These parameters are examined using the extended Kaya identity and the LMDI technique (Figure 6).

The technological effect was highly positive in Lincang and its counties, and it contributed the most to the SDG 6 composite index in Lincang City, at 61.84%. From 2015 to 2020, the water-use efficiency increased from 40.58 RMB/m³ to 48.37 RMB/m³ in Lincang City, and the water-use efficiency of the agriculture, industry, and service industries in Lincang increased by 42.06%, −16.45%, and 45.29%, respectively, while total water resource consumption decreased from 92.79 million m³ to 85.92 million m³. Fengqing County has the highest technology effect index, followed by Zhenkang, Shuangjiang, and Linxiang counties, with values of 0.43, 0.40, 0.35, and 0.34, respectively, all of which are above Lincang City’s average (0.29). Cangyuan County has the lowest technology effect index of 0.00, associated with fluctuating declines in water-use efficiency. The results show that improving water-use efficiency is vital to promote the sustainable development of water resources [31]. There is a great need to enhance water-saving and water-recycling technology where there are significant geographical disparities in Lincang City.

Lincang City also has a relatively high contribution of economic effects to the SDG 6 composite index, at 54.16%. In particular, Lincang City has seen an increase in economic development, with the city achieving full poverty eradication in 2019. The construction of water conservation facilities and advancements in sewage treatment technology have received adequate financial assistance. The percentage of public water supply penetration in Lincang’s rural areas improved from 74.40% in 2015 to 91.20% in 2020, while the rate of wastewater treatment increased from 65.51% in 2015 to 99.17% in 2020. Fengqing and Shuangjiang were the top two counties in the economic effect index, and Yun and Linxiang were the bottom two counties, with scores of 0.39, 0.33, 0.29, and 0.26, respectively. Those four counties had GDPs of RMB 14.73, 5.97, 17.02, and 13.09 billion, respectively, demonstrating that the economic effect of counties with large economic scales is greater than that of counties with small economic scales and that when the economic scale develops to a certain degree, the influence of the economy on the level of sustainable development of water resources declines.

The structural effect and population effect were generally negative, and the degree of influence in Lincang City and its counties was quite minimal. In Lincang City, the contribution of the population effect to the SDG 6 composite index is −11.96%; this negative impact is due to the large population decline. The city’s resident population dropped from 2.51 million in 2015 to 2.26 million in 2020, and the urban population dropped from 924.80 thousand in 2015 to 792.10 thousand in 2020, with the urban population accounting for more than half of the lost population. The population effect contributed 9.90% to the SDG 6 composite index in Linxiang County, where the resident population increased by 51 thousand, providing many human resources for agricultural upgrading, industrialization, and tertiary industry development, which has positively promoted the sustainable level of water resources. The total population of the other counties declined, and the demographic effect was entirely negative.

Lincang City’s structural effect contribution to the SDG 6 composite index is −4.03%, indicating a minor impact. Agricultural water-use efficiency was substantially lower than that of the industry and service sectors, the primary industry GDP accounted for 29.51% of Lincang City’s GDP in 2020, and agricultural water resource usage accounted for more than 80% of water resource usage, resulting in negative structural impacts. Among the eight counties, Linxiang, Gengma, and Zhenkang Counties had positive structural effects, with the average agricultural irrigation water resource usage lowered by more than 30%. This indicates that increasing agricultural water-use efficiency has a considerable impact on water resource consumption. The structural effects on other counties were all unfavorable, and even if agricultural water efficiency is declining, there is still much room for improvement.

4. Discussion and Recommendations

4.1. Multisource Data and Data Shortages in Small Areas

SDG 6 is central to the 2030 Agenda for Sustainable Development and has significant synergy with the other SDGs [32]. The current assessment of SDG 6 is primarily focused on SDG 6.1 and SDG 6.2, with minimal study on additional SDG 6 targets and few accurate progress assessments. The assessment of the SDG 6 composite index with multiple indicators may quantitatively evaluate SDG 6 achievement, compare the sustainable level of water resources among areas, estimate the gap between regional socioeconomic growth and the environmental threshold of water resources [33], and provide a reference for the improvement of water-related policies. Long-term reliable data sources provide a foundation for evaluating SDG 6 conditions, and this foundation can be used to assess the impacts of policy implementation. Data scarcity is a significant barrier to quantitative SDG 6 assessment. At the national level, 4/11 indicators of SDG 6 still have a methodology but no valid data. Problems of data inequality are found among countries, with even more substantial issues regarding data completeness and availability existing in small subnational regions [10,19]. SDG implementation occurs at smaller stages, such as prefecture-level cities and counties, and national policies for sustainable development must be more localized in order to be effective [11,12]. Solving the data scarcity problem and accurately analyzing the existing status of SDG 6 implementation in small-scale locations can serve as a guide for improving regional SDG 6-related policy.

To address the data shortage, researchers have supplemented existing datasets and official statistics with data from literature databases, citizen science data, enterprise data, and remote sensing data to fill some data gaps and meet the needs of finer spatial and temporal scales of research [17,34,35,36,37]. SDG 6 is applicable to a wide range of data sources. For water resources, statistical and remote sensing data are primarily used, augmented and corroborated by survey and web data. For the water environment, statistical and monitoring data are mostly used, supplemented and validated by remote sensing and survey data. For water ecology, statistical and remote sensing data are mostly used, with survey and web data serving as validation. Statistical data constitute the majority of the long-term stable data sources accessible for small-scale locations. Remote sensing data can be used as a key data source for SDG 6 assessment in small-scale areas as new remote sensing technology improves and satellite spatial and temporal resolution continues to increase [38,39]. Survey data, web data, and monitoring data can be used as supplements.

4.2. Influencing Factor of the SDG 6 Composite Index in Less Developed Mountainous Areas and Corresponding Measures

In terms of assessing the action degree of influence variables, this study is consistent with previous findings, with results indicating that the contribution of economic scale and technological level is significant [24,40], while the impact of population size and industrial structure is minimal [40,41]. Technical level and industrial structure are the influencing aspects that were found to be most consistent with previous studies in terms of action orientation, and improvements in technical level and industrial structure have a positive impact on the SDG 6 composite index [21,23,24,25]. Existing research differs in terms of economic scale and population size.

SDG 6.4.1 is lagging behind in Lincang City, and water-use efficiency in Lincang City is lower than the national or worldwide average level. Water-saving technologies are challenged by unbalanced and insufficient development, yet the technology effect substantially contributes to the sustainable level of water resources. Because resource endowments and socioeconomic development conditions differ across regions, specific project investment and policy development must be tailored to local conditions [24,27]. To resolve the contradiction between water resource scarcity, water environmental pollution, and socioeconomic development, developed regions mostly improve water-saving technology, rainwater utilization, sewage reuse technology, and sewage treatment technology [42,43]. Sewage collection and treatment facilities lack financial support in underdeveloped regions [44,45]. It is recommended that the Lincang Municipal Government encourage cross-regional technical cooperation, strengthen the strategic deployment of water-saving and water-recycling technology, and actively support the construction of various technology platforms and national high-tech enterprises. Linxiang County, with sufficient money, may improve rainwater use and sewage reuse, promote clean production, and upgrade sewage reduction technology at the source. While a county with limited resources can transfer technology from other regions, it should avoid transferring high-pollution, high-emission firms [24].

In terms of economic effects, some studies have found that economic effects increase water consumption and wastewater emissions [21,26,46], while others have found a nonlinear U-shaped link between economic scale and water resource sustainability [24]. The amount of water resources utilized and sewage discharged can be reduced through strict water resources management and legislation in undeveloped regions [47,48]. The scale of Lincang City’s economy is a positive factor of the SDG 6 composite index, with its total GDP ranking 11th among 16 cities in Yunnan Province, with more limited funds. Water-related policy reform and execution can help raise the degree of sustainable development for regional water resources.

Lincang City, as an underdeveloped city, must encourage high-quality economic and social development, industrial transformation and upgrading, and ensure the “quantity” and “quality” of economic development. The city’s climatic conditions are favorable for agricultural development, and agriculture contributes the most to the reduction in water resource usage [22]. According to the bulletin of Lincang City’s third national land survey, the arable land area of Yun County, Gengma County, and Yongde County accounted for 53.40% of Lincang’s territorial area; Gengma County, Fengqing County, and Yongde County accounted for 52.50% of the city’s plantation land; the four counties are suitable for developing large-scale agriculture, adjusting the agricultural planting structure, expanding the planting area of water-saving agriculture, promoting the use of water-saving appliances [21], and reducing the proportion of primary industries in other counties. Lincang City’s biodiversity area is 9302 km², accounting for 39.29% of the territory area, and five nature reserves are scattered within it. It is difficult to reverse industrial contamination in regions such as air, water, and soil. The city can guide the transformation of high energy- and water-consuming sectors to green ecological and high-value-added industries, and encourage the growth of clean production and low-water-consuming industries [46,49]. Simultaneously, it can support the growth of the tertiary industry, focus on the development of the ecotourism industry, optimize the industrial structure, and boost economic development.

Population growth has been found to increase water resource consumption and wastewater emissions [21,46]. The city’s population size has a negative influence, which may be related to a dramatic drop in the regional urban population. Increased population growth improves water resource use, while talent migration and worker shortages caused by population loss hamper socioeconomic development and technological progress. It is necessary to develop a reasonable population number based on the availability of water resources [46]. Given the current condition of significant population loss in Lincang, the talent introduction strategy should be revitalized to ensure the stability of the resident population and regional socioeconomic long-term development [50]. Furthermore, based on the development of stepped water prices, Lincang City needs to further publicize water-saving knowledge, increase the public’s voluntary water-saving awareness [22], and lower residents’ water demand.

4.3. Inputs and Benefits of Policies

Ecological economics has classified the three scales, distribution, and allocation difficulties confronting human economic and social growth as scale > distribution > allocation [51]. Water-use efficiency cannot be enhanced indefinitely in both developed and developing regions; once water-use efficiency reaches a certain level, the marginal cost of water savings increases and eventually tends toward a steady value [52]. With high levels of socioeconomic growth, there is an upper limit to saving water through technology upgrading and industrial restructuring. The more durable and long-term solution is to strengthen water resources management policies and develop water-saving awareness [22,53].

During the study period, Lincang City formulated and implemented policies on water resources and the water environment, such as, the drinking water projects in rural areas for SDG 6.1.1; the toilet revolution and “7 special actions” of patriotic health for SDG 6.2.1; the human settlements action plan (2016–2020) for SDG 6.3.1; technical guidelines for delineating source water protection areas and the “River Manager & District Manager” system for SDG 6.3.2; the implementation plan for the strictest water resources management system for SDG 6.4.1; the construction and maintenance of regional water conservancy projects for SDG 6.6.1; and a series of projects for water ecological civilization construction. Financial support is required for the implementation of SDG 6-related policies and projects. Among the policies related to SDG 6 indicators, SDG 6.6.1 requires the most funds, followed by SDG 6.3.2, SDG 6.3.1, and SDG 6.1.1, which require less funding and are closer in terms of funding requirements. SDG 6.2.1 requires the least funds, while SDG 6.4.1 is more dependent on government administrative evaluations.

The advancement of SDG 6 indicators reflects the effectiveness of policy implementation. Among the seven considered SDG 6 indicators of Lincang City, the five indicators of SDG 6.1.1, SDG 6.2.1, SDG 6.3.1, SDG 6.3.2, and SDG 6.4.2 have made significant progress, and are in the position of achieved or expected to achieve (I), SDG 6.4.1 still requires additional work, and SDG 6.6.1 has a varying declining trend. SDG 6 is primarily a livelihood project, and related policies and programs can boost people’s happiness. However, in less developed regions with limited funds, priority can be given to the policy implementations of SDG 6.2.1, SDG 6.1.1, and SDG 6.3.1, which require fewer funds, the policy implementations of SDG 6.3.2 and SDG 6.6.1 can be delayed, and SDG 6.4.1-related policies can be developed in accordance with local practice.

Lincang City has an abundance of water resources. To reduce the waste of water resources caused by “draft relying on water”, the performance of government support and direction is needed. In terms of water resources, the “Implementation Plan for the Strictest Water Resources Management System in Lincang City” should be firmly implemented, and the assessment system and harsh punishment mechanism for the initial property rights with regard to water resources and water-use efficiency should be improved [54]. In terms of the water environment, sewage discharge should be controlled at the source, an environmental tax should be collected, and water quality environmental monitoring should be increased [24]. In terms of water ecology, tight control of water resource development, the rational allocation of ecological water, and the maintenance of water ecosystem functions are needed [35]. Furthermore, Lincang City’s water-related policies must account for the relationship between the three dimensions of water resources, water environment, and water ecology; balance economic development and water ecological protection; carry out horizontal and vertical ecological compensation for water quantity and water quality; and promote the coordinated and orderly development of water resources, the water environment, and water ecology.

4.4. Deficiencies of This Study

The study period is 2015 to 2020, due to data availability, and the density data of public toilets are from 2015, 2017, and 2020. The change in the extent of water-related ecosystems has three data periods, from 2010 to 2015, 2015 to 2018, and 2018 to 2020. This study presents only one period for identifying influencing factors, which is from 2015 to 2020. The influencing factors are subject to a dynamic change process, and policy-driven factors are found in population, economics, structure, and technology. The next phase will involve extending the research period, carrying out a year-by-year comparison of the SDG 6 composite index’s influencing components, and conducting a quantitative analysis of the effects of policy on sustainable water resources.

5. Conclusions

This study used the “National Innovative Demonstration Zone for Sustainable Development” of Lincang City as an example; constructed an SDG 6 composite index with equal weights based on the SDG 6 targets; integrated multisource data such as statistical data, survey data, and remote sensing data; comprehensively evaluated SDG 6 indicators and the SDG 6 composite index; and used the LMDI method to analyze the degree of influence of economic, technological, population-related, and structural effects on the SDG 6 composite index in Lincang City and its eight counties. Combined with the spatial and temporal distribution characteristics of the SDG 6 composite index and resource endowment in each region, this study provides suggestions for optimizing the sustainable level of water resources in terms of influencing factors and policies, with a view to providing references for similar counties in South China and Southeast Asia.

In terms of data, this study was based on statistical data commonly available in small-scale areas, supplemented by remote sensing data and survey data, which effectively compensated for the incompleteness of the official statistical data of small regions. This study quantitatively assessed the current status of SDG 6 realization in small regions, revealing that SDG 6 evaluations based on multisource data are reasonably reliable and can provide a scientific basis for formulating water-related policies and helping realize SDG 6. Thus, a multisource data source based on statistical data, with remote sensing and other data as important supplements, can be used to conduct quantitative assessments of SDG 6 achievement in small locations.

In terms of the status of SDG 6 indicators’ implementation progress, the five indicators, SDG 6.1.1, SDG 6.2.1, SDG 6.3.1, SDG 6.3.2, and SDG 6.4.2 are in the position of achieved or expected to achieve (I); SDG 6.4.1 shows an obvious upward trend, but is still lower than the national or worldwide average level, which need to be strengthened (II); and SDG 6.6.1 has no reference standard for assessing realization status (IV). For SDG 6 progress, there is a spatial imbalance, Lincang City and the three counties of Gengma, Shuangjiang, and Yongde are in the position of achieved or expected to achieve (I), while Linxiang, Fengqing, Cangyuan, and Yun Counties are in the position of made progress but need to be strengthened (II).

In terms of three dimensions, the water resources index in wealthy counties is high, while the water environment and water ecology index in developing counties is comparatively high. And, the economic development level shows an opposite trend to the SDG 6 composite index; SDG 6 composite indexes are high in undeveloped counties. In terms of influencing factors of SDG 6 composite index, technology level and economic scale are significant positive drivers of the SDG 6 composite index, while structure and population have a less strong negative impact on the SDG 6 composite index. The influence variables’ action degree is more consistent with previous findings, and the influencing variables’ direction differ in economic scale and population size.

The improvement of water-use efficiency and the green transformation and development of the economy are important measures for the sustainable development of water resources, but there is a threshold for the improvement of water-use efficiency, thus, the government policies are important supplements to achieving SDG 6 on developing citys. For undeveloped cities similar to Lincang, with limited funds, the implementation of policies related to SDG 6.2.1, SDG 6.1.1, and SDG 6.3.1 can be prioritized, such as toilet revolution for SDG 6.2.1, drinking water projects for SDG 6.1.1, and the construction of an urban sewage network for SDG 6.3.1; and the implementation of policies related to SDG 6.3.2 and SDG 6.6.1 can be delayed.