*Article* **Observational Scale Matters for Ecosystem Services Interactions and Spatial Distributions: A Case Study of the Ussuri Watershed, China**

**Jian Zhang 1,\*, Hengxing Xiang 2,3, Shizuka Hashimoto <sup>1</sup> and Toshiya Okuro <sup>1</sup>**


**Abstract:** Understanding how observational scale affects the interactions and spatial distributions of ecosystem services is important for effective ecosystem assessment and management. We conducted a case study in the Ussuri watershed, Northeast China, to explore how observational scale (1 km to 15 km grid resolution) influences the correlations and spatial distributions of ecosystem services. Four ecosystem services of particular importance for the sustainable development of the study area were examined: carbon sequestration, habitat provision, soil retention, and water retention. Across the observational scales examined, trade-offs and synergies of extensively distributed ecosystem services were more likely to be robust compared with those of sparsely distributed ecosystem services, and hot/cold-spots of ecosystem services were more likely to persist when associated with large rather than small land-cover patches. Our analysis suggests that a dual-purpose strategy is the most appropriate for the management of carbon sequestration and habitat provision, and cross-scale management strategies are the most appropriate for the management of soil retention and water retention in the study area. Further studies to deepen our understanding of local landscape patterns will help determine the most appropriate observational scale for analyzing the spatial distributions of these ecosystem services.

**Keywords:** ecosystem service; observational scale; trade-off; hot/cold-spot; Ussuri watershed

#### **1. Introduction**

Understanding the impact of observational scale on the interactions and spatial distribution of ecosystem services is an integral part of mainstreaming the incorporation of ecosystem services knowledge into ecosystem management strategies at the science–policy interface [1–3]. In ecological studies, observational scale can be defined in several ways depending on the context. For example, in studies based on remote sensing and modeling, observational scale is usually described as having four components: (1) level of spatial detail, (2) numerical fraction, (3) spatial extent, and (4) process scale [4–6], which together indicate that ecological phenomena and objects each have their own distinct scale, or a range of scales, at which their characteristics and patterns are best observed [7,8]. An example to clarify the importance of observational scale selection is the competition between individual plants, which can be observed and discussed at the habitat scale but not at the regional or global scale [9,10]. At these larger scales, the break-out of pests or diseases (regional scale) and climate change (global scale) are more suitable topics for observation [11].

Ecosystem services, which, broadly speaking, are the benefits humans receive from the natural environment, are examples of ecological phenomena that have distinct observational scales at which their dynamics can be most efficiently observed and understood. In

**Citation:** Zhang, J.; Xiang, H.; Hashimoto, S.; Okuro, T. Observational Scale Matters for Ecosystem Services Interactions and Spatial Distributions: A Case Study of the Ussuri Watershed, China. *Sustainability* **2021**, *13*, 10649. https:// doi.org/10.3390/su131910649

Academic Editors: Margarita Martinez-Nuñez and Mª Pilar Latorre-Martínez

Received: 18 August 2021 Accepted: 22 September 2021 Published: 25 September 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the context of ecosystem service assessment, observational scale is usually defined as the scale at which samples are collected from the ecosystem service [8]. However, if observational scale is assumed to comprise several hierarchical levels, with each level associated with a scale break [9,10], it can be expected that an ecosystem service could have different characteristics at different observational scales. Recently, there has been increased interest in the issue of appropriate observational scale selection, which has resulted in the terms "scale effect" and "scale dependence" being used to describe the differences in ecological patterns and processes when observed at different scales [12,13]. However, despite ecosystem services currently being observed at many different observational scales [2], the effects of observational scale on ecosystem service assessment remain under-explored [8].

Identifying strategies that favor the management of multiple ecosystem services is an issue that is impacted by observational scale selection because ecosystem service management practices are developed based on feedback obtained from direct observation of ecosystem services [14–16]. Some scholars have recommended that ecosystem service assessments be made at the spatial scale where decision making occurs (e.g., at the local, subnational, national, regional, continental, or global scale) to provide assessments that are relevant to pre-defined social concerns [17]. However, such social concerns often span multiple spatial scales, and addressing those concerns requires an in-depth understanding of the complexities associated with multi-scale assessments [18]. Thus, elucidating how to identify the most appropriate observational scales at which to conduct ecosystem service assessments remains an important concern in the area of ecosystem services management.

Recently, a study in northeast China has suggested the need to reduce the ecosystem and service losses resulting from inappropriate policymaking and to improve the effectiveness of ecosystem management in the area [19]. However, a simple tally of ecosystem services would not reveal which factors are most important in this regard. Sometimes, factors that hamper the effectiveness of ecosystem management could be the intrinsic trade-offs among ecosystem services that were not anticipated during the management design phase [1]. Typical trade-offs among ecosystem services could occur across observational scales [20]. For example, strengthened food production observed at the site scale could impact habitat quality at a broader observational scale and potentially threaten water quality when observing at the watershed scale [21]. Thus, comprehending the impact of observational scale on ecosystem services' interactions is crucial for promoting the efficiency of ecosystem management.

In previous ecological studies, the concept of hot/cold-spots has been used to delineate areas of ecological importance [22]. In the context of ecosystem service assessment, hotspots indicate areas with a high concentration of ecosystem service value, whereas coldspots indicate the contrary. Greater understanding of the hot/cold-spots of ecosystem services has led to the spatial distributions of ecosystem services becoming essential topics of research [8,21]. However, most previous studies have used administrative divisions or the original resolution at which the data were collected as the observational scale rather than taking a multi-scale approach, which may have distorted the study outcomes [8]. Therefore, improving our understanding of how observational scale affects the spatial distributions of ecosystem services is expected to contribute to the development of improved ecosystem service management strategies.

One approach to better understand observational scales' impact on ecosystem services is to examine the robustness of ecosystem service assessments across various observational scales and determine how the characteristics of each ecosystem service are influenced by observational scale. Here, taking the Ussuri watershed in Northeast China as the study area, we examined how observational scale affects four ecosystem services in the area. For our observations, instead of using a conventional administrative scale (e.g., county, township), we used grids with resolutions ranging from 1 to 15 km. We then used our data to examine the changes in correlations between pairs of ecosystem services at increasing observational scales and how ecosystem service distributions react to different observational scales. Based

on our findings, we discuss policy-relevant implications of choosing a specific scale for ecosystem service observation and assessment.

#### **2. Data and Methods**

#### *2.1. Study Site and Ecosystem Services*

The Ussuri watershed in the Northern China Plain covers an area of approximately 61,460 km<sup>2</sup> and comprises a range of land covers and natural habitats, although it is predominantly plain in character (Figure 1A). The region is dominated by cropland (approx. 55% of the watershed in 2015) followed by green spaces (grassland, forest, and wetland; approx. 40% in 2015), but it also contains sparsely distributed rural and urban settlements (approx. 2.5% in 2015). The region is a typical peri-urban agricultural landscape that has been subjected to progressively industrialized farming, settlement development growth, and tourism exploitation (Figure 1B). A total of 15 cities/counties are entirely or partially located in the watershed, giving it a population of about 3,700,000. The Ussuri watershed is the largest component of the Sanjiang Plain (the largest marsh area in China and an important grain production base). However, the region has experienced tremendous wetland loss since the 1950s [23,24] and has been the recipient of strict management attention since 2000 [19,25,26].

Management for the rational and sustainable use of resources in the watershed is overseen by an assortment of government and public institutions. Alternative livelihood activities have been jointly implemented by the government of Heilongjiang, the Asian Development Bank, and the Global Environmental Facility to offer support and build consensus on common objectives for the management and use of forest/wetland resources, recognizing that the success of management strategies ultimately rests on the involvement of individuals [26]. The National Wetland Conservation Project of the National Forestry Administration is presently overseeing wetland resources management and is therefore responsible for the management framework [27].

Ecosystem services in the Ussuri watershed and adjacent regions are frequently mentioned in the literature, providing methodologies and data for diagnosing management problems [19,28,29]. In the present study, we examined four ecosystem services (i.e., carbon sequestration, habitat provision, water retention, and soil retention) that are related to the main concerns addressed by present ecosystem management strategies in the watershed (i.e., climate change mitigation, habitat rehabilitation, and headwater protection) [27].

**Figure 1.** Maps of the study site and elevation (m) (**A**) and land cover in 2015 (**B**). Forest: mixed forest, deciduous broadleaf forest, deciduous conifer, deciduous shrub, evergreen conifer, evergreen shrub, and tree garden; wetland: lake, reservoir, river, tree wetland, shrub wetland, herbaceous wetland, and canal; grassland: temperate steppe, tussock, and lawn; cropland: paddy field and dry farmland; built-up area: mine, transportation network, and settlements; other: barren land and desert [28].

#### *2.2. Methodological Steps*

Figure 2 summarizes the methodological steps of estimating and analyzing ecosystem services across varied observational scales. The following sub-sections describe each step in detail.

**Figure 2.** Diagram of methodologies used in this study.

#### *2.3. Land-Cover Classification*

Panchromatic images with 15/30 m spatial resolution collected in 2015 by the LAND-SAT Enhanced Thematic Mapper Plus (NASA) and Operational Land Imager (NASA) were used as the source data for land-use classification (Table 1). Cloudless satellite images (cloud coverage < 8%) collected in July, August, and early September were used for object-based classification. A total of 25 land-cover types were identified by using the multi-resolution segmentation and object-based classification approach of Mao et al. [19]. The accuracy of classification was assessed based on a total of 2388 historical ground-truth samples, affording an overall accuracy of 94%. Classification results were further compared with the land-use maps presented in recent studies to guarantee the applicability of the results for ecosystem service calculation [28–30]. For the quantification of ecosystem services, we used the original land-cover map with 25 land-cover types; for clearer display, the land-cover map for 2015 was further re-classified into six major land-cover types (forest, wetland, grassland, cropland, built-up area, and other) using the classification system of Wang et al. [28] (Figure 1B).


**Table 1.** Descriptions of data used in land cover classification and ecosystem service assessment. All the websites were accessed on 16th April 2020).

#### *2.4. Quantification of Ecosystem Services*

#### 2.4.1. Carbon Sequestration

Carbon sequestration service (CS) measures the capture and secure storage of carbon dioxide that would otherwise be emitted to, or remain, in the atmosphere and is of great importance to tackling global warming. We used the Carbon module of InVEST 3.2.0 to generate a distribution map of carbon sequestration in the study area [31] (Figure 3A). The amounts of carbon stored in four carbon pools (above-ground biomass, below-ground biomass, soil, and litter layer organic matter) for all land cover types was determined by using the formula:

$$\mathbb{C}\_{total} = \sum\_{i}^{n} \mathbb{C}\_{i} \times \mathbb{S}\_{i} \tag{1}$$

where *Ctotal* is the total amount of carbon sequestration in the Ussuri watershed, *C<sup>i</sup>* is the summed carbon density in the four carbon pools for land cover i, *S<sup>i</sup>* is the area of land cover *i*, and *n* is the number of land cover types we detected in the land-cover classification phase (*n* = 25). Carbon density data were obtained from Xiang et al. (Table 1) [32].

**Figure 3.** Distribution maps for the four ecosystem services examined in the present study. (**A**) Carbon sequestration; (**B**) habitat provision; (**C**) soil retention; (**D**) water retention.

#### 2.4.2. Habitat Provision

Habitat provision services (HP) are relevant to both permanent and transient populations of wildlife [33] and are extremely important for maintaining biodiversity [34]. We used the Habitat Quality module of InVEST to rate habitat quality on a scale of 0 to 1 (higher value indicates higher habitat quality) (Figure 3B). This module uses the following formula to estimate the spatial extent, vegetation type across a landscape, and state of degradation by combining information on land cover and threats to biodiversity:

$$Q\_{\rm xj} = H\_{\rm j} \left( 1 - \left( \frac{D\_{\rm xj}^z}{D\_{\rm xj}^z + k^z} \right) \right) \tag{2}$$

where *Qxj* is the habitat quality of pixel *x* in land-cover type *j*, *H<sup>j</sup>* is the expert knowledgebased habitat quality score obtained from the InVEST 3.2.0 User's Guide, D*xj* is the state of degradation of pixel *x* in land-cover type *j* [31], and K is the half-saturation constant taken as 0.3 [29]. Note that the exponent *Z* assigned to D*xj* and K refers to the scaling parameter, which was taken as 2.5 [29]. The data prepared for the estimation of *Qxj* comprise a landcover map, the sensitivity of each land cover to each threat, and a list of threats and their features. The sensitivity of each land-cover type to each threat and the weight of their impact were obtained from Xiang et al. [29].

#### 2.4.3. Soil Retention

Soil retention (SR) (Figure 3C), here defined as the difference between the potential worst case soil erosion under bare soil conditions and the actual soil erosion calculated by using the Universal Soil Loss equation [35], was calculated as follows:

$$SR = R \times K \times LS \times (1 - \mathcal{C}) \tag{3}$$

where *R* is rainfall erosivity (MJ mm, ha−<sup>2</sup> ha−<sup>1</sup> yr−<sup>1</sup> ) (calculated based on daily precipitation), *K* is soil erodibility (t h MJ−<sup>1</sup> mm−<sup>1</sup> ), *LS* is slope length gradient factor (calculated based on DEM), and *C* is the percentage of vegetation coverage. Data sources of daily precipitation, soil erodibility, and DEM are from the National Earth System Science Data Center (http://www.geodata.c, accessed 16 April 2020.) (Table 1).

#### 2.4.4. Water Retention

Water retention service (WR) measures soil's ability to retain water. A two-step process was used to estimate water retention (Figure 3D). First, water yield was calculated using the Annual Water Yield module of InVEST and the following formula [31]:

$$\mathcal{WY}\_{total} = \sum\_{n}^{i} (P\_i - AET\_i) \times A\_i \tag{4}$$

where *WYtotal* is annual total water yield (t yr−<sup>1</sup> ), *P<sup>i</sup>* is annual precipitation (mm), *AET<sup>i</sup>* is evapotranspiration (mm), *A<sup>i</sup>* is area (km<sup>2</sup> ), and i is the pixel of interest. The calculation of *WYtotal* followed the detailed methods presented in InVEST 3.2.0 User's Guide. We then used the value of *WYtotal* to calculate the water retention capacity using the method of Wang et al. [36] as follows:

$$\text{WR}\_{\text{capacity}} = \text{MIN} \left( 1, \frac{249}{V} \right) \times \text{MIN} \left( 1, \frac{0.9 \times TI}{3} \right) \times \text{MIN} \left( 1, \frac{K\_{\text{sat}}}{300} \right) \times W Y\_{\text{total}} \tag{5}$$

$$\text{TI} = \text{Log}\left(\frac{Surface}{SoilDepth \times PercentSlope}\right) \tag{6}$$

where *WRcapacity* is the annual average water retention capacity (mm); *V* is the velocity coefficient, which is a constant value (dimensionless); *T I* is the topographic index

(dimensionless); and *Ksat* is the saturated hydraulic conductivity (mm/d). "*Sur f ace*" in Formula (6) is the number of grids in the watershed, "*SoilDepth*" is the soil thickness (mm), and "*PercentSlope*" is the slope percentage (Table 1). The calculation methods of *T I* and *Ksat* were referenced from Li et al. [37], and the data sources and biophysical parameters used in Formulas (4)–(6) were obtained from Xiang et al. [29].

#### *2.5. Method of Analysis*

#### 2.5.1. Observational Scales

The observational scales employed in this study were developed on the basis of management scales (scales at which ecosystem management is formally implemented in the Ussuri watershed) and scale hierarchies [10]. The management scales were determined by identifying the principal managers of ecosystem services. Usually, principal managers of ecosystem services refer to individuals who are formally incentivized to engineer the landscape to manage the ecosystem, and the institutions that develop rules to regulate access to ecosystem services [8]. In the Ussuri watershed, the principal managers of ecosystem services were (1) farmers who grant part of their croplands for wetland and forest restoration or manage their farmland for soil condition and grain yield, and (2) the government bureau that supervises national nature reserves therein [19,26,29]. The management decisions and implementations often occur at the individual level, government level, or some compromised level between them. Therefore, the 1 km grid, which was considered to approximate the spatial scale at which individual land-use management occurs, was designated as the finest observational scale. Meanwhile, the 15 km grid, which was considered to approximate areas of land similar in size to the smallest nature reserve (the Qixinghe wetland reserves that covers an area of 208 km<sup>2</sup> ) and the spatial scale at which national directives and local interventions are applied, was designated as the coarsest observational scale. In addition, 13 intermediate observation scales (scales between the 1 km and 15 km grids where sampling is conducted and measurements are taken) were added to clarify how features of ecosystem services react to changes in observational scale [8,38]. These intermediate observational scales were simulated with a 1 km grid interval (i.e., from 2 to 14 km grid resolution). All 15 grids were generated in ArcGIS 10.7 using the Fishnet tool.

#### 2.5.2. Correlations between Ecosystem Service Pairs

A correlation analysis was conducted to examine the suitability of using a dualpurpose management strategy (a management strategy that regulates two ecosystem services at the same time) for each pair of ecosystem services. We hypothesized that (1) a dual-purpose management alignment will occur at an observational scale when there is synergy between a pair of ecosystem services, indicating that the two ecosystem services can be managed simultaneously, and (2) a dual-purpose management mismatch will occur at an observational scale when there is a trade-off or no correlation between a pair of ecosystem services, indicating that the two ecosystem services cannot be managed simultaneously.

Pearson's parametric correlation test was performed in IBM SPSS 22 to identify potential synergies and trade-offs among pairs of ecosystem services. Min max normalization was applied to nondimensionalize the data before analysis. The Shapiro–Wilk test was used to verify the normality of the data before the correlation analysis. If the coefficient between pairwise ecosystem services was significant (*p* < 0.05), the correlation was considered valid. A significant negative coefficient was considered to indicate the existence of a trade-off (one ecosystem service increases while the other decreases), and a significant positive correlation was considered to indicate the existence of a synergy (both ecosystem services increase) [39]. The correlation analysis was performed at each of the predetermined observational scales to examine the changes in ecosystem service interactions across observational scales. When using the coarsest observational scale (15 km grid), the maximum sample size of ecosystem services was 229 in this study. Therefore, the ecosystem service samples at each observational scale were all set to 229 to avoid the impact of varied sample sizes on the significance test. The sampling points at all observational scales except for

the coarsest observational scale were randomly selected using ArcGIS 10.7 and manually edited to avoid an over-concentrated distribution.

#### 2.5.3. Spatial Patterns of Ecosystem Services

To quantify the spatial patterns of the ecosystem services, we used Anselin's local Moran's indicator [40]. This indicator is used to decompose a global statistic into its constituent parts [40], and then each part is classified as a hot-spot (high–high clusters), cold-spot (low–low clusters), outlier (high–low or low–high clusters), or non-significant spot. We used this approach to identify areas of the watershed where different management approaches could be successful [22]. The hot-spots indicated areas where high-value ecosystem services are highly aggregated, suggesting that a reactive management approach would be appropriate; the cold-spots indicated areas where low-value ecosystem services are highly aggregated, suggesting that a proactive management approach would be appropriate.

A step-wise process was used to analyze the spatial clusters of the ecosystem services. First, the spatially explicit evaluation of the ecosystem services derived by InVEST simulation was re-calculated in ArcGIS for all observational scales. Then, Anselin's local Moran's indicator [40] with queen contiguity was calculated in ArcGIS and compared at all observational scales. Finally, the hot/cold-spots were screened out and counted. A local regression (LOESS) curve fitting was performed to visualize the general trends as the observational scale was changed.

#### **3. Results**

#### *3.1. Correlations between Ecosystem Service Pairs at Different Observational Scales*

Correlation analysis revealed only one ecosystem service pair, CS–HP, with significant, high synergy at all observational scales (r = 0.860–0.923; Table 2). A mix of synergies and trade-offs were found for the other ecosystem service pairs depending on the observational scale.

**Table 2.** Pearson correlation coefficients for pairs of ecosystem services at different observational scales. Synergies are shown in green, trade-offs are shown in red, and no correlations are shown in yellow. \*\* *p* < 0.01; \* *p* < 0.05. CS, carbon sequestration; HP, habitat provision; SR, soil retention; WR, water retention.


Synergies were observed for CS–SR, HP–SR, and WR–SR. For CS–SR, low synergy was observed at 1–4 km grid resolution (r = 0.306–0.423) and 9–14 km grid resolution (r = 0.291–0.323), and high synergy was observed at 15 km grid resolution (r = 0.674). For HP–SR, low synergy was observed at 1–4 km grid resolution (r = 0.296–0.373) and high

synergy was observed at 15 km grid resolution (r = 0.630). For WR–SR, low to high synergy was observed at 10–15 km grid resolution (r = 0.304–0.613).

Negative correlations, which indicate trade-offs between ecosystem services, were observed for CS–WR and HP–WR. For CS–WR, trade-offs were observed at 3–8 km grid resolution (r = −0.422 to −0.306) but synergy was observed at 15 km grid resolution (r = 0.288). For, HP–WR, trade-offs were observed at 3–14 km grid resolution (r = −0.580 to −0.440).

#### *3.2. Spatial Distributions of the Ecosystem Services at Different Observational Scales*

We used the local Moran's indicator to examine the spatial distribution of each of the four ecosystem services across the 15 observational scales. Figure 4 shows the distributions of hot-spots (high–high clusters), cold-spots (low–low clusters), outliers (high–low or low–high clusters), and non-significant spots. When the observational scale was increased, adjacent grid areas of any cluster type were merged and then smoothed before the new grid area was assigned a new cluster type. This resulted in the scaling behavior of the different ecosystem service clusters across the observational scales being characterized as either merge–shrink or merge–expand. For example, for WR, small hot-spots were scattered across the study area at the finest observational scale (1 km grid resolution); however, as the observational scale was increased, these areas merged together and increased in size, resulting in a merge–expand behavior.

**Figure 4.** Spatial patterns of ecosystem service clusters (99% confidence interval) at four representative grid resolutions. CS, carbon sequestration; HP, habitat provision; SR, soil retention; WR, water retention.

Figure 5 shows the changes in the proportion of land classified as hot/cold-spots for each ecosystem service with increasing observational scale; LOESS curves are shown to clarify the general trends. The proportion changes of 1 km vs. 15 km grid resolution observation were calculated. For CS, HP, and SR, the proportion of cold-spots decreased with increasing observational scale, with decreases of 25.2% for CS, 3.3% for HP, and 7.3% for SR (Figure 5A–C). In contrast, for WR, the proportion of cold-spots increased by 4.5% (Figure 5D). For CS and HP, the proportion of hot-spots decreased by 8.6% and 15.1%, respectively (Figure 5A,B), whereas for SR and WR, the proportion of hot-spots increased by 13.0% and 21.8%, respectively (Figure 5C,D).

**Figure 5.** Changes in the proportion of land classified as hot/cold-spots with increasing observational scale for the four ecosystem services. (**A**) Carbon sequestration. (**B**) Habitat provision. (**C**) Soil retention. (**D**) Water retention. Gray curves are fitted curves that indicate the general trend (95% confidence interval).

Shifts in whether hot- or cold-spots were dominant were observed for CS, HP, and WR (Figure 5A,B,D). For CS and WR, there was a greater proportion of cold-spots compared with hot-spots at lower grid resolutions, but a greater proportion of hot-spots compared with cold-spots at higher resolutions; this reversal occurred at 10 km grid resolutions for CS and at 8 km grid resolution for WR. For HP, the proportion of hot-spots was greater than that of cold-spots at 1 km grid resolution, but this situation reversed from 2 km grid resolution.

#### **4. Discussion**

Understanding the spatial distributions and interactions of ecosystem services is crucial for the development of effective ecosystem service management strategies. The scales at which ecosystem services are observed or monitored fundamentally shape this understanding. Here, we conducted a case study of the area of the Ussuri watershed within the border of the People's Republic of China to examine how observational scale affects the mapping of ecosystem services and their pairwise synergies and trade-offs. Based on our findings, we discuss how best to select the most appropriate observational scale for ecosystem service assessment and management.

#### *4.1. Synergies and Trade-Offs across Observational Scales*

First, we determined pairwise correlations among the four ecosystem services to examine how their correlations change with increasing observational scale (Table 2). The authors of previous case studies conducted for Quebec, Canada [8], and the Ningxia Hui Autonomous Region, China [12], concluded that most pairwise correlations are robust across different observational scales and that significant correlations are more often observed at finer than at coarser observational scales. However, based on our present data, we do not agree with these previous conclusions, although it must be noted that ecosystem service selection and the biophysical context of the study area must be taken into consideration when comparing the present and previous data.

In our analysis, we found that the correlation between CS and HP was robustly synergistic across all observational scales. Our distribution maps for CS and HP revealed that these ecosystem services are distributed extensively throughout the study area and are frequently coincided with certain types of land cover (Figure 3). For example, areas of high CS value and HP suitability score were found to be areas classified as forest and wetland, which is natural given that these two land-cover types are characterized by their large carbon pools and abundance of wildlife [19,41,42]. Turner et al. noted that land cover types with extensive distribution tend to change evenly across observational scales because the local configuration does not influence scaling [43]. We consider that Turner's summing applies equally to correlations between ecosystem services that are extensively distributed.

In contrast to the distribution maps for CS and HP, those for SR and WR revealed that these ecosystem services were unevenly and sparsely distributed throughout the study area. Areas of high SR were primarily areas of forest at high elevation, and that of WR shared a similar distribution but appeared as more fragmented patches (Figure 3). In addition, we found that the correlations involving these ecosystem services were a mix of synergies, trade-offs, and no correlations (Table 2). For example, for CS–WR, trade-offs of various strengths were observed from 3 to 8 km grid resolution; no correlations were observed at 1, 2, and 9–14 km grid resolutions; and low synergy was observed at 15 km grid resolution.

Collectively, the results of our correlation analysis suggest that the distribution of an ecosystem service is an indicator of how robust its pairwise correlations are across observational scales; that is, correlations among extensively distributed ecosystem services are more likely to be robust, whereas those of ecosystem services with patchy distributions are more likely less robust, suggesting that more judicious selection of observational scale would be required when conducting assessments of these ecosystem services.

Assuming that observational scale equals the spatial scale at which future management occurs, our data suggest two implications with regard to the use of a dual-purpose ecosystem service management strategy. First, the robust synergy between CS–HP at all observational scales suggests that dual-purpose management of these two ecosystem services may be a cost-effective means of providing synergistic enhancements to both ecosystem services, and that such a management strategy could be applied at any scale. Evidence supporting our viewpoint could be found in several previous studies, where Zheng et al. [44] and Xiang et al. [29] have reported that widespread natural habitat restoration has increased biodiversity in the Sanjiang plain. The restoration of high-diversity ecosystems on degraded or abandoned land merits further implementation for its potential to provide increased CS [45].

Second, despite the present dual-purpose management of WR and SR in the Ussuri watershed by the Grain for Green Project [19], which is overseeing the reforestation of marginal cropland to reduce soil erosion and water loss, we only found synergies between these two ecosystem services at the coarser observational scales examined (10 to 15 km grid resolution; Table 2), suggesting that the current dual-purpose management approach is not suitable at all observational scales. Improving SR by improving erosion control in specific areas also improves WR [46,47]; however, WR often relies on large-scale landscape patterns and watershed dynamics [48–50]. These characteristics of the two ecosystem services are

consistent with our findings of a dual-purpose management mismatch between WR–SR at the fine and intermediate scales. One means of resolving this mismatch could be to increase the number of soil erosion control sites in the Ussuri watershed to create a more extensive distribution pattern; however, soil erosion control is a costly investment when implemented over a sizeable spatial scale. Therefore, we suggest introducing a cross-scale strategy for the management of SR and WR [8]. That is, we suggest incentivizing the participation of individual managers or management institutions in improving WR via implementing erosion control, which will reduce the workload and financial burdens placed on government-level WR management. Meanwhile, the government-level managers need to carry out plans to specify the spatial extent in the Ussuri watershed to guide individual and institutional managers to put erosion control into effect. It is also noteworthy that the plans should be based on the in-depth knowledge of the local landscape and spatial pattern of ecosystem services.

Despite our observation of moderate synergies for CS–SR and HP–SR at the 15 km grid resolution, more low-level synergies and no-correlations occurred for them during the scaling-up of observation (Table 2). Further research is needed to explore the potential of dual-purpose management for such ecosystem service pairs in the Ussuri watershed.

#### *4.2. Ecosystem Service Clusters at Different Observational Scales*

Mapping hot/cold-spots provides straightforward information for determining where to implement different management options. Cold-spots are areas where there is a lower possibility of harvesting an ecosystem service, suggesting that the ecosystem service therein is better left undisturbed. In contrast, hot-spots are areas where there is a higher possibility of harvesting an ecosystem service, suggesting that the ecosystem service therein may potentially be harvested with high acceptance by management institutions and other stakeholders.

Here, we found that observational scale substantially influenced the spatial distributions of hot/cold-spots in two ways (Figure 4). First, increasing the observational scale altered the location and size of the hot/cold-spot clusters, such that the land-cover type associated with some of the clusters also changed. For example, whereas CS hot-spots associated with large patches of forest and cold-spots associated with continuous cropland were relatively preserved across the observational scales, those associated with wetland in the central part of the study area at fine observational scales (1–3 km grid resolution) had disappeared at the intermediate and coarse observational scales. Similar findings were also observed for HP, SR, and WR, although the clusters associated with continuous cropland, large patches of forest, and sizeable water bodies were relatively persistent across the observational scales.

Second, changing the observational scale altered the proportion of land covered by the cold/hot-spots, and for CS, HP, and WR, increasing the observational scale altered whether it was hot- or cold-spots that were the dominant cluster type (Figure 5). Both the trends and the spatial extent of the ecosystem service clusters varied at different observational scales. We speculate that regardless of whether or not an ecosystem service is extensively distributed throughout an area, observation at certain scales will fail to capture the real spatial pattern of hot/cold-spots because land-cover features shape the distribution of these clusters as well as how these clusters react at different observational scales. A widely used principle that emerged in the field of mapping ecosystem services is observing ecosystem services at the scale of administrative, policy, and management boundaries to facilitate the relevance between the assessment output and management decision making [8,19]. This principle implies that the mapping of ecosystem services should be based on the question being asked and the type of details required. We argue that, indeed, the assessment needs to match with the need of decision makers, but, more importantly, the assessment should capture the complexities of ecosystem services. Therefore, the selection of an appropriate observational scale for ecosystem service cold/hot-spots can only be interpreted by taking into consideration the social-ecological heterogeneity of the local landscape. The landscape pattern in the Ussuri watershed is a combined result of the gradual encroachment of cropland on wetland since the 1980s, the implementation of forest and wetland conservation measures around 2000 [19], and the watershed's distance from the urban agglomeration of Harbin. Therefore, understanding the underlying mechanisms that shape local socialecological conditions can help define the appropriate scales for observing the spatial patterns of ecosystem service hot/cold-spots.

We argue that, indeed, the assessment needs to match with the need of decision makers, but, more importantly, the assessment should capture the complexities of ecosystem services.

#### *4.3. Methodological Limitations and Future Study*

Many previous studies have elucidated the factors that affect the correlations and distributions of ecosystem services (e.g., the accuracy of input data and aggregation method used), but observational scale has not yet been examined [51–53]. We found here that the effects of changing observational scale are similar to those produced by smoothing to create an approximate function that captures important patterns in a data set while leaving out noise and outliers. That is, we found that when the observational scale was changed, data points of value were modified so that individual points higher in value than the adjacent points were reduced, and points that were lower in value than the adjacent points were increased, leading to compromised values. Therefore, observational scale should be considered a double-edged sword [47,54]. On the one hand, because it clearly determines the fitness and coarseness of the data and patterns that are obtained, it brings convenience to decision makers by letting the assessment results feed the management goal; on the other hand, it brings challenges with respect to accuracy because increasing the observational scale may introduce redundant or inaccurate data.

Other limitations of the present study are the simulation method and quality of input data used to measure the ecosystem services. The InVEST model provides a straightforward approach to map and monitor habitat quality that can be used as an estimate of biodiversity [55]. However, when there are multiple definitions of natural habitats and threats (i.e., habitat patches would be defined differently by large mobile wildlife compared to rare species of plants) [53], different spatial distributions or other land cover-based data may be obtained. Although we conducted our analysis using the best available data to provide qualified results, our use of average inventory values for carbon density associated with specific land covers and default model parameters in the WR and SR simulations may have failed to capture the effects of management types, climate factors, and geographic traits. In addition, validation is often absent in ecosystem service mapping and monitoring, so a better understanding of the uncertainties involved in the models used is needed [56].

Regarding future study, we suggest including more aspects of ecosystem services, such as the societal values and consumption of ecosystem services, in multi-observational scale analysis to select suitable observational scales [8,15]. The societal values of ecosystem services influence the rules and actions that alter the provision and access to ecosystem services [15], and the consumption of ecosystem services implies appropriate management incentives [8]. Comprehending how these aspects of ecosystem services vary across different observational scales allows identifying potential conflicts in ecological/environmental management, particularly among different stakeholders and managers, thereby building an effective, accountable, and inclusive framework to guarantee the sustainability of ecosystems in the Ussuri watershed.

#### **5. Conclusions**

Here, we present a case study conducted in the Ussuri watershed, Northeast China, in which we examined how observational scale affects ecosystem service assessment. We examined four ecosystem services (regulating and supporting services) at 15 observational scales (1 to 15 km grid resolution), which included two approximate scales at which ecosystem management is formally implemented. Correlation analysis revealed that ecosystem service distribution may be an indicator of the robustness of the correlation between pairs

of ecosystem services across various observational scales. That is, a correlation is likely to be robust when an ecosystem service is extensively distributed across the study area (i.e., CS and HP in the present study), but not robust when an ecosystem service is sparsely distributed (i.e., SR and WR). Based on our findings, we suggest that a dual-purpose management strategy is most appropriate for the management of CS–HP in the Ussuri watershed, and that cross-scale management strategies are the most appropriate for the management of WR and SR.

The pattern of ecosystem service clusters (cold/hot-spots) across the various observational scales was associated with the size of land cover patches. Indeed, complex networks of ecosystem service clusters were observed in association with dispersed land covers at finer observational scales, but these networks became less complex, homogenous clusters at coarser scales. In contrast, the ecosystem service clusters associated with continuous land covers were persistent across the various observational scales. Thus, selecting an appropriate observational scale for delineating ecosystem service clusters should take into account the local landscape patterns and an understanding of local social-ecological complexity.

Though our results have the potential to improve the management decision making in ecosystem services, there are still several limitations that need to be addressed in future study. Technically, enhancing the sensitivity of the models used results in better accuracy of the input data, and the use of local parameters could help achieve a better estimation of ecosystem services. Moreover, including societal values and consumption of ecosystem services in the multi-observational scale analysis will bring better negotiation and coordination between stakeholders and managers at different scales, thereby ensuring the sustainable development of the Ussuri watershed.

**Author Contributions:** J.Z. and H.X. conceived the study and generated the datasets. J.Z. performed the data analysis and prepared the manuscript. T.O., S.H. and H.X. provided critical feedback and edited the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by a scholarship from the Japan Ministry of Education, Culture, Sports, Science and Technology and the National Natural Science Foundation of China, grant number "41671219", the Scientific and Technological Development Program of Jilin Province, China (No. 20200301014RQ).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** We thank the Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences; the National Earth System Science Data Center (http://www.geodata.cn); Geospatial Data Cloud (http://www.gscloud.cn), and National Meteorological, Information Center (http://data.cma.cn/user/toLogin.html), for their data support. We are grateful to the all the editors and reviewers for their constructive feedback and edits.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


## *Article* **Proposal of a Conceptual Model to Represent Urban-Industrial Systems from the Analysis of Existing Worldwide Experiences**

**Carmen Ruiz-Puente**

INGEPRO Research Group, Department of Transport and Projects and Processes Technology, University of Cantabria, 39005 Santander, Spain; ruizpm@unican.es

**Abstract:** The adoption of Industrial Symbiosis (IS) practices within urban areas is gaining interest due to the environmental impacts entailed by the development of cities. However, there is still a lack of knowledge about how the relationships between industrial and urban areas can be modelled. In this context, this research aimed at posing a conceptual model to understand and represent Urban-Industrial Systems (UIS). To this end, a set of worldwide previous UIS experiences were overviewed to identify the agents, dynamics, and collaboration opportunities that characterize them. The multi-perspective analysis of these cases indicated that UIS are complex systems, which means that they are autonomous, self-organized, responsive, nonlinear, and willing to consolidate their resilience. As such, Agent-Based Models (ABM) were suggested to be the most suitable approach for their representation.

**Keywords:** complex systems; industrial ecology; urban-industrial symbiosis; urban-industrial systems; urban metabolism

#### **1. Introduction**

Today, around 50% of the world's population lives in cities. This figure is expected to rise up to 70% by 2050 [1]. To face this rapid growth, cities must be planned to harmonize the three pillars of sustainable development, namely economy, environment, and society [2]. The explosion of industrial activity and population over the last two centuries have caused serious environmental degradation [3], putting current societies in a situation in which production processes must be reformulated to be efficient in the use of energy and natural resources. An industrial transformation towards sustainability is of great opportunity for a step change. Digital technologies in flexible manufacturing [4] and in-depth comprehension of the transformation patterns for decision making in resource dependent cities [5] can support this evolution.

Industrial Ecology (IE) and Urban Metabolism (UM) are two concepts closely related to this situation. IE is an interdisciplinary research area aimed at mimicking natural ecosystems in the production of goods and services, thereby trying to close energy and resource cycles [6]. UM concerns the exchange of resource flows and information between urban settlements and their surroundings [7]. As a bridge between both terms, the concept of Urban-Industrial Symbiosis (UISym) emerged to try to close the cycles between industrial and urban areas by promoting resource and energy exchange to each other [8]. The main obstacle behind this interaction is the need for strong investment to implement these symbiotic networks.

Some investigations have been developed throughout the years to address different aspects related to Urban-Industrial Systems (UIS). Both Sun et al. [9] and Dong et al. [10] focused on determining the ecological benefits derived from the implementation of an UIS in Liuzhou (China), obtaining important reductions in resource mining and waste disposal. Sun et al. [11], Bian et al. [12], and Shah et al. [13] extended the scope of these types of studies by introducing the concept of eco-efficiency, thus exploring the economic impacts and geographic feasibility of implementing an UIS in the cities of Shen-yang and Guiyang

**Citation:** Ruiz-Puente, C. Proposal of a Conceptual Model to Represent Urban-Industrial Systems from the Analysis of Existing Worldwide Experiences. *Sustainability* **2021**, *13*, 9292. https://doi.org/10.3390/ su13169292

Academic Editors: Margarita Martinez-Nuñez and Mª Pilar Latorre-Martínez

Received: 18 July 2021 Accepted: 17 August 2021 Published: 18 August 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

(China) and Ulsan (Korea). Other investigations have recently applied more specific tools or methods to design UIS. Fan et al. [14] developed a Pinch Analysis approach for waste integration, and Yong et al. [15] developed an approach for energy integration in UISym sites. The results obtained from each demonstration case study revealed the potential of the extension of the Pinch method for the engineering design of UIS.

The trend of UIS-related research reveals that most studies provide methodological contributions to account for the environmental benefits derived from the analysis of individual case studies while discussing the factors that hinder the implementation of symbiotic practices [16]. In this context, this research analyses the behavioural patterns observed in the main international UIS experiences, whereby the main agents, dynamics, and collaboration types involved in the interrelation between urban and industrial areas are brought together. A methodology based on research on design has been conducted to assess the main international UIS experiences that are in operation nowadays [17]. To this end, Section 2 of this article includes an overview of the symbiotic networks of seven selected real UISym case studies, and Section 3 contains the identification and description of the main features resulting from a multi-perspective analysis of these systems. Then, in Section 4, the fundamentals of a new conceptual model based on the insights obtained from the analysis are raised. Finally, Section 5 contains the main findings of the study, highlighting both their implications for this research field and future lines of action to address the limitations of this study.

#### **2. Overview of Real Urban-Industrial Symbiosis Case Studies**

The methodological approach used in this work is based on research on design [17]. With the aim of developing a UIS conceptual model, several worldwide UIS case studies were included in the research for this analysis. These case studies needed to have been successfully created, implemented, and operated. There were seven UIS case studies that met this condition and that were selected to analyse the development and implementation of synergies between industrial and urban areas, focusing on the challenges found for the materialization of this interaction. A total of three of these cases were in Europe (Forth Valley, Kalundborg, and Norrköping), one was in North America (Londonderry), and three were in Asia (Suzhou, Kawasaki, and Liuzhou). Other experiences such as Devens (U.S.), Ginebra (Switzerland), or Salaise-Sablons (France) were not considered due to their early stage of operation and/or limited data availability. First, a characterization of the case studies was conducted in terms of the symbiotic exchanges that occurred in each experience. Table 1 summarizes the main characteristics of the case studies that were overviewed, including their location, starting year, and operation scheme (do-nor(s), flow(s), and recipient(s)). The donor is the agent involved in the symbiotic exchange that supplies any waste flow, secondary outputs, or financial capital. The recipient is the agent involved in the symbiotic exchange that receives any waste flow, secondary outputs, or financial capital. Every exchange connection is denoted by the flow(s) supplied from the donor to the recipient(s). As it can be observed for each case, any agent (e.g., the Suzhou case, WWTP, incineration plant, cogeneration plant) can perform a dual role as a donor or a recipient indistinctly.

The industrial park of Suhzou was selected by the Chinese government to promote clean and renewable energy. There are two main groups of synergies in this park: the symbiosis among wastewater treatment, sludge, and cogeneration plants and the exchanges involving heating, cooling, and electric energy. Recovered water is mainly used for cooling in cogeneration plants. However, treating large volumes of water involves great amounts of sludge, which are generally disposed in landfills due to the absence of standards in this sense. To palliate this, a drying sludge plant was built next to a Wastewater Treatment Plant (WWTP) and a cogeneration plant in 2011. The processing of wet sludge and its subsequent use as fuel for generating electricity through cogeneration saves 12,000 tCO2eq/year. The ash stemming from the incineration of sludge is used to produce construction materials. During the drying process, 90,000 t/year of condensate are also sent to cogeneration, which

serves to save 1 M RMB/year in terms of water and heating costs. The steam generated during cogeneration goes through cooling towers to produce water for the Moon Bay district. Hence, 3390 t CO2eq, 8000 t CO2, 70 t SO2, and 70 t NO<sup>x</sup> per year can be saved as well as 50,000,000 RMB/year. associated with maintenance works.


**Table 1.** Characteristics of the main worldwide experiences on the development of Urban-Industrial Systems (UIS).

<sup>1</sup> All others: agents involved in each UIS case study; <sup>2</sup> WWTP: Wastewater Treatment Plant.

Forth Valley couples Edinburgh and the petrochemical complex of Grangemouth, which is the greatest industrial area in Scotland. Moreover, Forth Valley includes four large electric plants, a cement plant, two oil companies, and two paper factories. Its synergies include the reuse of shells for roads and inert waste for aggregates and soil as well as the recycling of home appliances. Other synergies related to wastewater, heating, sludge treatment, and energy are also being investigated. The drying of sludge results in pellets, which can provide fuel to supply 30,000 households. Power plants recover and reuse about 500,000 t/year of fly ash and solid ash, resulting in GBP 988,000 in savings in 2004. The cement plant in the park uses 3 M of waste tires and 20,000 t of recycled liquid fuel produced

by other companies, which enables the saving of more than 40,000 t of fossil fuels and the reduction nitrogen oxide emissions. A power plant was established to burn 110,000 t/year of bird faeces, generating 81 GWh/year of electricity to supply 20,000 households. The remaining ash stemming from the process was used as high-quality fertilizer.

The Kawasaki case study emerged because of the interest of the Japanese government to form eco-cities. To this end, it funded five facilities related to reuse paper and valorise plastics as inputs for use in both blast furnaces and in the manufacturing of ammonia and concrete moulds. A typical example of by-product exchange in Kawasaki is the use of slags from the production of steel as raw materials in the manufacturing of cement. These steelworks are fed with iron and non-ferrous materials from an appliance recycling facility, whilst cement plants are recycling the sludge from urban wastewater to replace clay as well as wood, plastic, tires, and oil wastes to substitute carbon. Kawasaki has the first paper recycling plant to achieve zero emissions. The city government manages and supervises the collection of Municipal Solid Waste (MSW) and Industrial Water (IW). Non-recyclable MSW are transferred to incineration plants, the ash of which is either reused by cement plants or disposed in a landfill. Besides the incinerators, the government also controls five collection centres and one transportation centre for MSW.

Londonderry is using eco-industrial development to deal with the negative effects of rapid growth. The inhabitants of the city have mobilized to preserve its agricultural heritage and to promote adequate environmental and cultural development. A recycling company approached a creamery to acquire its plastic waste and rinse them using grey water. This was the first step in the Londonderry eco-industrial park project, in which every member would be audited in terms of energy efficiency, water conservation, product management, materials usage, etc. A private investor owned the land and financed the development of the park. A 720 MW combined cycle power plant was installed in this park and was built underground. Soil extracted during its construction was used to develop the regional Manchester airport. In addition, the plant is cooled with 15,140 m3/day of treated wastewater that is pumped from the WWTP in Manchester. However, the inclusion of companies in using the steam and residual heat from the power plant did not come to fruition, resulting in the power plant going to receivership in 2004.

Kalundborg arose from the premise of conserving natural reserves and improving the economy. Since 1960, it has been an important industrial centre for the country due to its large scale eco-industrial park configuration. In 1961, the power plant of the city decided to replace the use of groundwater with surface water from a lake, prompting a shift in the awareness of resource valuation. The system is formed by five main components: a power plant (600 employees and a carbon-based capacity of 1500 MW), a refinery (250 employees and a capacity of 3.2 Mt/year.), a gypsum board company (160 employees and an annual production of 14 M m<sup>2</sup> ), an international pharmacy company (1400 employees and annual sales of \$2000 M), and the municipality of Kalundborg, which provides heating to 20,000 inhabitants and supplies houses and companies with water. The relationships among these agents resembles a food chain, including actions such as supplying houses, greenhouses, and aquaculture farms with heat obtained from generators and by reusing biological sludge for use as fertilizers or calcium sulphate and fuel gas to manufacture gypsum boards.

Liuzhou is a Chinese city whose economy is headed by the steel and automotive industry. The situation in this city is complex, involving different companies with a variety of economic, environmental, and social interests. This fact hindered the spontaneous creation of a symbiotic network. For these reasons, the government boosted the testing of Liuzhou as a laboratory to assess the potential benefits of IS. A steel company performs as a central node in the symbiotic system, such that it is surrounded by other industries with a high potential for material and energy exchange, such as power plants, cement plants, chemicals plants, etc. There are nine types of materials, energy sources, and wastes that can be exchanged, including blast furnace slags, treated slags, metallurgical gas, waste heat, desulfurization by-products, steel, plastics, tires, and ash. As a result, this UIS can

save more than 2.4 M t of materials and 0.9 M t CO2eq of energy through exchanges, while reducing solid waste by 3.4 M t and CO<sup>2</sup> emissions by 2.3 M t.

Norrköping is characterized by strong renewable energy developments and close cooperation among industrial companies that create self-organized clusters. This municipality was a pioneer in installing and operating an urban heating system. Today, it feeds a cogeneration plant with MSW (25,000 t/year.), which provides urban heat and industrial steam water. A refinery produces distillery slops that become either forage to be used in agriculture or substrate for a biogas plant. The development of this network has been aided by the commitment of the municipality with the environment, the creation of an urban heating system, the development of a cogeneration plant, and the biogas demand by the transport sector. There are future plans for promoting new synergies in Norrköping, such as a sawmill, which would be used to produce wood pellets.

#### **3. Multi-Perspective Analysis of Urban-Industrial Systems**

After the characterization of the UIS case studies described in Section 2, a further investigation was done to compare the behavioural patterns of the experiences. A systematic multi-perspective analysis was realized to identify and describe the main features of these systems. This section accounts for the main results, which are summarized in Table 2.

#### *3.1. Types of Urban-Industrial Systems*

The main aspect defining the existing types of UIS is the nature of their investor. Private investors fund and facilitate contact among companies to obtain economic benefits. Instead, public investors are national or local governments that use funds or taxes to promote cooperation among companies to bring together the economic, social, and environmental benefits. In both cases, the figure playing the role of the investor can take part in the management of the exchange networks. In the case of public investment, the process can take place with or without the support of a facilitator, who is an intermediary assuming the promotion and management of the network.

According to the case studies in Table 1, public investment (governments as donors) was the approach taken in Suzhou, Forth Valley, Kawasaki, Kalundborg, Liuzhou, and Norrköping, whilst Londonderry was the only city financed through private investment (private investor as a donor). Regardless of the source, it is always necessary to have external funding to boost and maintain UIS. Otherwise, the arrangement of cases such as these is very complicated and is unlikely to happen spontaneously. Investors are usually motivated by environmental needs, such as the valorisation of urban waste generated in municipalities (e.g., Norrköping) or the increasing concern of industrial pollution (e.g., Suzhou).

#### *3.2. Agents in Urban-Industrial Systems*

Regardless of the type of UIS, there are common agents in these systems, as demonstrated in Table 2. First is the industry, whose main aim is to transform raw materials into products. SMEs have a similar purpose but work with lower business volumes and mainly perform as waste recipients. The municipality is home to a population, generating Municipal Solid Waste (MSW) and wastewater. Rural areas relate to zones devoted to agriculture and livestock, whilst power plants deal with the production of electric energy.

There are three other agents focused on valorising either materials, water, or energy. The former seeks to provide new uses for wastes, whilst water valorisation concerns treatment plants aimed at purifying wastewater from the industry and the municipality, such that it can be reused without damaging the environment. Finally, energy valorisation stands for both the incineration of MSW and the drying of sludge. Incineration causes a volume reduction in waste, which is transformed into ash that can be used in industry as additives. Thermal energy can also be obtained throughout this process. As for sludge drying, it consists of dehydrating the wet sludge stemming from WWTP and converting it into pellets to be used as fuel by other agents.


**Table 2.** Characteristics of the agents involved in Urban-Industrial Systems (UIS).

<sup>1</sup> MSW: Municipal Solid Waste; <sup>2</sup> IW: Industrial Water; <sup>3</sup> IWW: Industrial Wastewater; <sup>4</sup> UWW: Urban wastewater; <sup>5</sup> WWTP: Wastewater Treatment Plant.

#### *3.3. Dynamics in Urban-Industrial Systems*

The mechanisms to create and successfully operate the UIS analysed in the worldwide experiences took a long-term approach. Overall, the operation of UIS can be divided into initial, intermediate, and advanced stages of development. The initial stage, led by either a public or private investor, serves to encourage the required participants to form a basic exchange network in contact with each other. During the intermediate stage, the symbiotic network grows through the addition of more partakers. In the advanced stage, the system is managed by either public or private investors to maximize its benefits.

The agents become involved in the system at different stages. The components participating in the system from the beginning of the process are only municipality and industry. They are both at the core of UIS not only for conceptual reasons, but also because of the amount of waste they generate, which can be reused by other agents that enter the system in the intermediate stage, such as SMEs, power plants, and rural areas. The latter join the system to strengthen the symbiotic network and obtain benefits from it. Although they are not as essential as those parties who are involved in the initial stage, their role is crucial in terms of association and cooperation.

The agents related to resource valorisation are created complementarily to the existing symbiosis network in the advanced stage to improve it and minimize the amount of waste. They work as intermediaries among agents that could not cooperate otherwise, resulting in new options for the reuse of waste. Despite the differences between the intermediate and advanced stages, this breakdown is not always put into practice. For instance, these steps emerged at the same time in the third case study that was overviewed (Kawasaki) since there was an interest in rapidly densifying the network and closing material loops.

#### *3.4. Collaboration in Urban-Industrial Systems*

The most common means of collaboration observed in the main worldwide experiences (Table 1) concern the exchange of materials, water, and energy. This does not have to always be this way; other cases lacking heavy industry might be more active in sharing infrastructure and/or services [34].

The collaboration identified in the case studies can be grouped as to the type of flows exchanged between the donors and recipients summarized in Table 1. These flows were organized and allocated to each type of agent as input and output flows, as presented in Table 2. There are four distinct main categories: materials (MSW—plastic waste, organic waste, scrap, glass; industrial waste—plastics, scrap, ash, slags, fuel, fertilizers, wet sludge), water (urban wastewater, industrial wastewater, treated water, water for cooling purposes), energy (thermal energy), and economy (money, investment, funding, tax reduction, control of service management, benefits from exchanges).

Contacts among the parties involved usually stem from meetings among representatives of the industries promoted by the investor, in which the potential benefits of collaboration are shown. In this sense, computer tools are used to facilitate cooperation, such as synergy databases, where companies can search for potential exchanges. It is also common to form councils involving representatives of both the industry and the municipality to take charge of the development and management of the symbiotic network. As a summary of all the aspects covered in this section, Table 2 shows the agents involved in UIS as well as their role, aims, flows (inputs and outputs), and type.

#### **4. Proposal of a New Conceptual Model**

The genesis of the case studies summarized in Figure 1 demonstrates similar characteristics with Planned Eco-Industrial Parks (PEIPs), which seek compatible activities with potential to cause symbiotic networks with the financial support of an external agent [35]. This coincides with the role played by the investors, who promote and coordinate the creation of these experiences. However, this situation would appear to change with the increasing autonomy of park agents, and they start searching for their own benefits with

time, to the extent that they end up resembling the dynamics of Self-Organizing Symbiosis (SOS) [36] rather than those of PEIPs.

**Figure 1.** Graphical summary of the type, size, and time of the relationships among the agents involved in the main worldwide Urban-Industrial Systems (UIS).

This unsteady condition highlights the need for elaborating a model for their systemic conceptualization. To this end, the analysis of the behavioural patterns in the main UIS experiences reported worldwide was first, as listed in Figure 1. The dynamics observed in these case studies enabled their subsequent examination as complex systems. Then, a list of indicators was proposed to measure the eco-efficiency of the agents involved in the UIS. The last step consisted of the selection of a modelling method according to the characteristics of UIS as complex systems.

This course of action is in line with the conclusions drawn from recent studies in the field of UIS to emphasize the development of this kind of approach as a key line of research to develop in the future. For instance, Lu et al. [37] pointed out that the lifecycle of a perspective UIS, which is closely related to the evolutionary behaviour of these complex systems with time, must be considered. Moreover, Fan et al. [14] underlined to the importance of implicating all of the agents and flows involved in UIS. In the same vein, Bian et al. [12] referred to the creation of comprehensive systemic models to account for the interactions among UIS sectors while proposing an eco-efficiency indicator approach to account for the effects and contributions in UIS.

#### *4.1. Behavioral Patterns in Urban-Industrial Systems*

Figure 1 provides a graphical scheme of the agents identified in Table 2 and their relationships to each other, based on the case studies summarized in Table 1. These agents are represented using different sizes (small, medium, large) depending on the number of

exchanges in which they participate, while highlighting the stage of development when they entered the system. Industry, energy production, and municipality agents are depicted as the largest in terms of exchanges, whereas SME, energy valorisation, and agriculture agents are the smallest. Material and water valorisation agents participated in a number of exchanges in between and therefore are represented by as being of medium-sized. The scheme also indicates the frequency with which each flow (materials, water, and energy) is exchanged, specifying its correspondence to the case studies under analysis (e.g., the energy production agent exchanges energy with industry and municipality in five case studies). The inspection of Figure 1 reveals that most of the interactions take place in the last stage. This is due to the ad hoc purpose of the material, energy, and water valorisation agents entering the system in this step since they are aimed at complementing the symbiotic network and increasing the number of relationships in order to reduce waste and close loops.

Most of the cases that were overviewed (2, 5, 6, and 7), where both industry and municipality participate from the initial stage through flow exchanges, are governed by a similar pattern in their dynamics. Their initial stage is deeply rooted due to both the exchange of different waste flows between industry and municipality and the support of public investors. Companies devoted to the production of electric energy are essential in the intermediate stage since they can contribute to increasing the efficiency of the system, either directly (interacting with industry and/or municipality) or indirectly (through other agents, such as in the case of agriculture and livestock in Case 2). Finally, the agents in charge of the valorisation of materials are indispensable in the advanced stage to absorb waste and, therefore, to densify the network. The other agents involved in this stage depend on the priority flows to be reused, resulting in energy and water valorisation plants such as incinerators (e.g., case 7, Norrköping) and WWTP (e.g., case 5, Kalundborg).

As for Case 3, the rush of the public investors to transform Kawasaki into an eco-city provoked a leap in the Urban-Industrial System, whereby the intermediate stage was omitted. Instead, valorisation agents, which usually belong to the advanced stage, were straightforwardly incorporated to intensify the exchange network. This experience further highlights the great importance of these components in consolidating UIS; however, the haste in strengthening the network might lead to some agents, such as power plants, whose role may eventually yield better results in terms of efficiency, being disregarded.

Cases 1 and 4 were not initially conceived as UIS experiences. In the case of Suzhou, public investment was targeted at improving energy efficiency. In the end, this original goal resulted in an ecological urban-industrial development. Instead, Londonderry started with the aim of creating an eco-industrial park through material exchanges in the industry. The municipality adhered to the network in the subsequent steps and did not conduct direct exchanges with the industry. These cases could be defined as late UIS since the incorporation of the municipality takes place after the initial stage and because the exchanges with the industry are indirect.

#### *4.2. Urban-Industrial Systems as Complex Systems*

A system can be defined as a set of interactive elements [38]. Complex systems have a structure formed by the following characteristics: (1) autonomous, (2) self-organized, (3) responsive, (4) not governed by linear patterns, and (5) willing to consolidate their resilience [39]. Autonomy is especially relevant for agents in low hierarchical levels because they are responsible for boosting the symbiotic networks. Table 1 highlights how industry plays a different role from the remaining players, setting relationships with a variety of agents, including the link between the refinery industry with the municipality (Kalundborg), the bio-industry with rural areas based on agriculture and livestock (Norrköping), or even between itself as the steelworks and cement plant pair (Kawasaki).

Self-organization stems from the agents within the system that do not require any external support. Thus, every agent belonging to the UIS increases the number and intensity of its relationships with the remaining components in the system [40,41]. In this sense, investors are important in promoting the existence of these cooperation actions. As for their reaction capacity, the basic components of UIS (people, companies, etc.) respond differently to changes in their environment. In the first case study that was overviewed (Shuzou), the network emerged in response to the taxes and fees created by the government due to the high pollution produced in the municipality, which resulted because the bad practices of heavy industries were favoured in the area. Instead, the situation in Norrköping was very different since the goal was to improve the energy efficiency of the system. The agents involved in this case used the support of the government to intensify and promote new cooperative relationships to each other.

Cooperation among companies is boosted by the existence of confidence and previous agreements; however, these aspects do not follow linear patterns, which results in the generation of unpredictable conduct in the system. In turn, this involves additional complexity for the development of interactions [42,43]. The investigation of the case studies compiled in this work suggests that some of the experiences were initiated in a similar manner. However, different networks emerged later on, and the benefits achieved by these networks differed from each other. In addition, many complex systems are also adaptive. The behaviour of the basic components in adaptive systems can evolve with time, providing a certain reaction capacity against changes in the environment through learning mechanisms.

This factor results in the last characteristic of complex systems, which concerns their resilience. UIS are capable of both dealing with technological and social challenges as well as admitting new agents and eliminating exchange flows [44]. An example of resilience in UIS is the collaboration among the agents, which enhances the robustness, adaptability, and flexibility of the system. In the case of Kawasaki, the Japanese government considered this feature and promoted the creation of ad hoc agents to ensure the resilience of the system with time. Based on the examination of all of these characteristics, it can be concluded that UIS are complex and adaptive systems.

#### *4.3. List of Eco-Efficiency Indicators*

The concept of eco-efficiency, which arises from the consideration of environmental impacts throughout the lifecycle of products and the willingness to reduce them them, can be useful to value changes in UIS. It seeks to produce more and pollute less [45]. Given the differences in the data quality among economic, environmental, and social parameters established by international organizations to monitor sustainable development as well as the complexity in adapting them to the specifics of UIS, it is difficult to produce consistent aggregations to evaluate the performance of the whole system.

Instead, the World Business Council for Sustainable Development (WBCSD) proposes a series of indicators that can be used to measure the eco-efficiency of a system [46]. Table 3 includes some of these indicators, which were grouped as follows: applicable to the industry and municipality, applicable to the whole UIS, and exclusive for the industry and municipality. The first group is valid for the two main agents in the system (industry and municipality) and can be used to obtain valuable information about the changes produced in any of them thanks to a symbiosis process. They account for energy consumption, the conversion of urban and industrial wastes into by-products, the amount of greenhouse gas emissions produced, and the reduction of waste taxes.

The second group, which concerns all of the agents in the UIS, has a predominantly environmental nature. Energy consumption includes electricity, fossil fuels, biomass, wood, solar and wind power, etc. Water represents all of the freshwater provided by the public network or that is obtained from surface or groundwater sources as well as water used for cooling purposes. The third indicator measures the amount of acid gas or steam emitted into the air as a result of fossil combustion and reactive processes.


**Table 3.** List of indicators to measure the eco-efficiency of Urban-Industrial Systems (UIS) [46].

The last group considers exclusive indicators for the industry and municipality. The first subgroup accounts for quantitative indicators related to industrial goods, products and services, and substances destined for disposal. In addition, it addresses economic aspects concerning sales, discounts, and income. The subgroup associated with the municipality includes the wealth generated per resident inhabitant, the monitoring of diseases derived from pollution, and the increase in population. In the end, the joint consideration of the indicators in the three groups represents resource flows (materials, water, and energy) and social, environmental, and economic aspects whose management is expected to improve due to the proposed urban-industrial model.

#### *4.4. Selection of Modelling Method*

The selection of a modelling method needs to be consistent with the characteristics of the system to be represented [47]. In particular, the creation of a mathematical model to describe UIS is very difficult due to, among other things, the uncertainty, emergence, or adaptability of these complex systems [48]. In this sense, there are some analytical methods used to represent complex systems. A brief description of the most relevant ones and a valuation of their suitability to be applied for modelling UIS can be summarized as follows [49]:


Figure 2 summarizes the workflow for selecting the most suitable tool for modelling UIS. As introduced above, the specifics of UIS suggest that ABM is the best option. The first reason supporting this is based on the existence of complex nonlinear discrete interactions among the agents. This means that the actions of an agent can be altered by others, such that their description through traditional methods (e.g., statistical approaches) might be difficult. There is a variety of exchange flows in UIS (Figure 1); however, this does not mean that an agent can only exchange one flow type. For example, the incinerator in the third case study (Kawasaki) only generates ash, whilst this same facility exchanges both material and thermal energy flows in Norrköping.

There are not only different types of agents in UIS, but some of the same kind exhibit different attributes. Traditional modelling approaches represent agents with average characteristics, which is far from the real situation in UIS. Table 2 compiles the ten types of agents involved in UIS. The case of industry is especially enlightening in this sense because it encompasses a variety of different types (cement, chemical, pharmacy, etc.) and has very different relationships to the remaining agents, regardless of their specific field of activity.

The topology of the interactions among the agents in the system is heterogeneous and complex. This is especially relevant for social processes, which involve learning and adaptation. ABM enables realistic topological modelling, which is required to explain the aggregated behaviour of the system. The high number of potential interactions among agents and the variety of flow types that can be exchanged in UIS hinder their modelling.

Finally, UIS have a complex and stochastic behaviour that changes with time, which precludes its forecast in advance. Hence, it cannot be approached using equations or transition rates. This circumstance is clearly represented in the adaptive capacity of the Norrköping case study. In this situation, the government invested in improving a power plant to enable feeding it with biofuel; however, the situation evolved to a greater exploitation of the system with the passage of time, including MSW and densifying the network by promoting different flow exchanges.

**Figure 2.** Flowchart for the selection of the most suitable tool for modelling Urban-Industrial Systems (UIS). Adapted from Heckbert et al. [49].

#### **5. Conclusions**

In accordance with the aims initially set in this research, a conceptual model to understand and represent Urban-Industrial Systems (UIS) has been presented. To this end, the patterns of different worldwide urban-industrial symbiotic networks were observed. Such patterns served to outline a model capable of accounting for all of the characteristics of UIS as complex systems, including their stages, dynamics, participating agents, and exchange flows. The analysis also emphasized the importance of public and private investment to boost the creation and development of UIS.

The examination of existing UIS enabled the identification of the main agents and flows involved in symbiotic networks as well as their importance. The comprehension of these dynamics led the posing of an analytical model to represent UIS reliably and systematically, giving insight into the relationships among the agents within the system as they join the network throughout its different stages. The proposed model is intended to improve the eco-efficiency of UIS through the consideration of a series of indicators that represent the technical, social, environmental, and economic characteristics of the agents involved.

The trends and characteristics observed in the analysis as well as the conceptualization of UIS modelling are important contributions for encouraging the development of new experiences focused on the creation of UIS. The inferences extracted from the investigation of existing cases provide useful information for potential investors, emphasizing both the profile of the agents required to form a symbiotic network and the time in which each of

them should enter the system. Although a preliminary conceptual model has been inferred, its robustness would benefit from widening this systematic research on more successful cases. The representation of UIS through the application of the proposed model is the main line of research that needs to be developed in the future in order to continue building a road map to maximize the benefits of promoting symbiotic interactions between urban and industrial areas in terms of sustainable development. Companies, policy makers, and interested stakeholders could explore the patterns and the effects that their strategies and policies could have on the hopefully successful reconfiguration and implementation of a new urban-industrial model that is able to use waste and secondary outputs as resources. A smart digitalization of industry and cities can become key in supporting and accelerating this transformation towards sustainability.

**Funding:** This research was funded by the Spanish Ministry of Science, Innovation and Universities, grant number DPI2017-88127-R (AEI/FEDER, UE).

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The author declares no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **References**

