Enhancing synergies and reducing trade-offs among the 17 Sustainable Development Goals (SDGs) and related 169 targets [1], which were adopted by world leaders from 193 countries, is fundamental to realize the ambitious and transformative vision of socioeconomic and environmental sustainability on the planet Earth. Synergies emerge when multiple SDGs/targets are improved simultaneously. Trade-offs occur when efforts for achieving SDGs/targets hamper other SDGs/targets [2][3][4][5][6]. Since the adoption of the 2030 Agenda in 2015, many studies have focused on evaluating synergies and trade-offs among the SDGs/targets [7][8][9][10][11], yet less attention has been paid to the effects of actions on SDG interactions across geographical boundaries [6,[12][13][14][15][16]. Recent studies noticed different impacts of consumption levels [12,13] and international trade [17] on the SDGs/targets between developed and developing countries, and another study indicated energy use changes in one local place may influence progress toward SDGs of other areas [15]. In the socioeconomically and environmentally metacoupled planet [18,19], special attention to evaluating SDG synergies and trade-offs across boundaries is urgently needed. As processes at one place may enhance or hinder sustainability in both surrounding and distant areas [20], the SDGs may fail to make significant progress at the global level without a better understanding of how sustainable development efforts are metacoupled locally, regionally, and globally.
A new integrated framework, based on the concept of metacoupling [21], was introduced to evaluate SDG synergies and trade-offs within and across boundaries explicitly [6]. As a new frontier for global sustainability [22][23][24][25][26][27][28], the metacoupling framework addresses socioeconomic-environmental interactions within a system (i.e., intracoupling) and across adjacent (i.e., pericoupling) or distant systems (i.e., telecoupling) [21]. The metacoupling framework has been applied to many important issues, such as environment [29], energy [30], soil conservation [31], food trade [32], and fishery [22], and across different scales, such as smallholders [25], regional watershed systems [33], national energy network [30], and marine system at global scales [23]. It was only recently applied to SDG interactions and emphasized that the flows (e.g., tourism and trade) affect SDG synergies and trade-offs across boundaries [6,17].
The new framework [6] filled the research gap that spillover effects on SDG synergies and trade-offs across geographic boundaries were less understood [16], because one advantage of using the new framework to study SDG interactions is emphasizing spillover systems, defined as the systems that affect and/or are affected by the interactions between sending systems (e.g., exporting country) and receiving systems (e.g., importing country) [34,35]. In other words, spillover systems identify the areas that do not participate in a particular process but are influenced by it. Examining SDG synergies and trade-offs only within or between sending and receiving systems overlooks the complex interactions that may exist beyond the two systems. Moreover, the demonstrating cases of tourism to and panda loans from the Wolong Nature Reserve [6] are two examples of many metacoupled flows (of information, energy, people, organisms, goods, and matter) that increasingly connect the world. The flows influence SDG synergies and trade-offs when several targets or SDGs are positively or negatively affected by the flows from start to end, across boundaries [6,17].
Tourism, one of the most typical flows of people, is an important sector of the global economy and accounts for nearly 10% of jobs worldwide [36]. The contribution of tourism is also recognized in SDG 8 (decent work and economic growth) and target 8.9: "By 2030, devise and implement policies to promote sustainable tourism that creates jobs and promotes local culture and products [1]." Tourism in protected areas is among the fastest-growing sectors of the world's tourism industry [37,38]. The world's terrestrial protected areas alone receive approximately 8 billion visits annually, generating a direct economic impact of US $600 billion [39].
Concerning the movement of wildlife, an increasing number of species have been translocated due to conservation purposes and trade. More than 5000 animal species and 29,000 plant species are traded as live specimens, fur coats, and dried herbs across country borders [40]. Legal international wildlife trade alone is estimated to be worth over US $320 billion per year [41], and the demand for wildlife has grown rapidly, involving 233 countries and territories worldwide [40][41][42]. For instance, a total of 88,081 records of trade of live specimens for non-commercial purposes (e.g., educational, scientific, breeding, botanical gardens, and zoos) were reported between 1975 and 2017, and at least 49 countries and territories had more than 100 trade records (Figure 1). Moreover, wildlife trade has arguably contributed to zoonotic disease worldwide [43], such as the ongoing COVID-19 [44], which has infected over 23 million cases and resulted in more than 800,000 deaths as of 23 August 2020 [45]. Evaluating how those flows of people and wildlife may influence SDG synergies and trade-offs is urgently required, and can contribute to further understanding the drivers behind the synergies and trade-offs among SDG/targets across boundaries [6,[12][13][14][15][16]. Building on the framework and Wolong case study [6], at global level, we synthesize 22 selected peer-reviewed studies focused on tourism and wildlife translocations, aiming to demonstrate how to use this framework to guide the analysis of SDG synergies and trade-offs within the focal system (e.g., a protected area as a receiving system of tourists) and across spillover systems (e.g., neighboring towns and villages of the protected area).
Here, we applied the new framework [6], which integrates the conceptual structure of metacoupling [21] and SDGs and targets [1], to assess synergies and trade-offs among SDGs within and across different systems due to tourism and wildlife translocations (Figure 2). [21]. (b) A protected area, as a focal system, receives tourists from sending systems (origin of the tourists). The effects of the metacoupled flows of tourists go beyond the directly related SDG 8 (decent work and economic growth), and may enhance or compromise other SDGs both locally (i.e., within the receiving system) and across spillover systems (e.g., neighboring areas). (c) The focal system can be a sending system of wildlife translocations. The effects of the wildlife translocations go beyond the directly related SDG 15 (life on land) and may enhance or compromise other SDGs both locally (i.e., within the sending system) and across spillover systems. (d) The focal system also can be a receiving system (e.g., zoo), depending on the direction of flows of wildlife translocations. The arrows between SDGs (in subfigures b, c, and d) indicate interactions (i.e., synergies or trade-offs). Subfigures b, c, and d were modified from [6]. Credit (SDG symbols): United Nations [1].
Under the conceptual framework of metacoupling (Figure 2a) [21], within a coupled human and natural system (CHANS), subsystems interact through various flows between them. Cross-boundary flows (e.g., tourism and wildlife translocations) between the sending systems (e.g., origins of wildlife) and receiving systems (e.g., destinations of wildlife) may affect the spillover systems (which affect and/or are affected by the metacoupled flows between sending and receiving systems). Agents are the stakeholders, such as individuals, households, organizations, and governments, that facilitate or hinder the metacoupling. Causes indicate why couplings occur (e.g., environmental, socioeconomic, political, cultural reasons), and effects are the consequences of couplings [21].
Efforts (e.g., tourism development) toward achieving a specific SDG in a certain place (focal system) may enhance or compromise other SDGs both in the focal system and in other systems (e.g., spillover systems), generating direct or indirect SDG synergies and trade-offs [6]. Concerning tourism in protected areas, effects of the tourism flow go beyond the directly related SDG 8 (decent work and economic growth) within the focal system (e.g., a protected area), and may enhance or compromise other SDGs both locally (i.e., within the receiving system) and across spillover systems (e.g., neighboring areas) (Figure 2b). Regarding wildlife translocations, the effects of the wildlife translocations go beyond the directly related SDG 15 (life on land) and may enhance or compromise other SDGs both locally and across spillover systems. The focal system of wildlife translocations can be a sending system (e.g., an export country of wildlife) or receiving system (e.g., a zoo for wildlife translocations) depending on the direction of flows (Figure 2c,d). Within a focal system, besides the interactions between different SDGs, synergies or trade-offs between different targets of an individual SDG may exist [6]. Across spillover systems, besides the synergies or trade-offs between different SDGs, flows may hinder or favor the same SDG/target of the focal system, generating the synergy/trade-off within an individual SDG across different systems. Moreover, by tracking where the flows start, progress, and end, indirect SDG synergies and trade-offs can be identified, because the SDGs/targets influenced by the initial flow (e.g., tourism) may, in turn, enhance or compromise other SDGs/targets through associated flows (e.g., money), both within the focal system and across spillover systems [6].
Guided by the general procedure of using the framework [6], we conducted a literature review and analyzed tourism in protected areas and wildlife translocations globally. We first searched for relevant studies that presented effects beyond the boundaries of focal systems. We then assessed the directions and magnitudes of the flows of tourism and wildlife translocations as well as the features of focal and spillover systems. We linked the quantitatively measured effects with the SDG indicators [46] based on the framework (Figure 2).
We gathered literature on global tourism and wildlife translocations through comprehensive searches on Web of Knowledge and Google Scholar. The literature was published between 1 January 1990 and 3 May 2018. The search terms we used were: tourism, wildlife trade, wildlife translocation, wildlife introduction; protected areas, national parks, nature reserves, forest parks; neighboring areas, surrounding areas, other places, beyond boundaries; effects; and spillover system. Our purpose was to illustrate the widespread SDG interactions across spillover systems due to extensive metacoupled tourism and wildlife translocations, rather than to exhaustively search all previous studies. Therefore, we stopped searching after we identified ~30 publications in each of the six continents.
The searches returned 96 peer-reviewed journal articles, 24 book chapters, and 66 reports (17 by government agencies, e.g., Great Barrier Reef Marine Park Authority, 32 by intergovernmental agencies, e.g., CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora), and 17 by non-governmental organizations, e.g., World Wildlife Fund).
To further locate detailed data for our study, we then read through these journal articles, book chapters, and reports to see whether: (1) the cases involved costs and/or effects beyond the boundaries of focal systems; (2) the costs and/or effects were quantitatively evaluated; and (3) the costs and/or effects were associated with SDG targets or indicators [46]. Based on these three criteria, our screening yielded 22 cases for final analysis, including 12 cases of tourism (Table 1) and 10 cases of wildlife trade and translocation (Table 2). The quantitative costs and/or effects of 22 cases were acquired from 26 peer-reviewed articles (24 journal articles, 1 conference paper, and 1 discussion paper), 4 peer-reviewed books, and 4 reports (peer-reviewed and published by the government or intergovernmental agencies).
The results from analyzing the 22 cases (Figure 3) from around the world show a total of 47 linkages among 23 targets of 10 SDGs, including 33 synergies and 14 trade-offs (Tables 1 and2). 1 and2. Credit (SDG symbols): United Nations [1].
The 12 cases of tourism (Figure 3 and Table 1) indicate that, beyond the directly related SDG 8 (decent work and economic growth), tourism in protected areas enhanced or compromised other SDGs, including SDGs 2 (zero hunger), 9 (industry, innovation and infrastructure), 12 (responsible consumption and production), 14 (life below water), 15 (life on land,) and 17 (partnerships) within focal systems, and SDGs 1 (no poverty), 8, 9, 15, and 17 across spillover systems.
3.1.1. Within Focal Systems, Synergies and Trade-Offs between SDG 8 (Decent Work and Economic Growth) and Other SDGs Varied in Three Ways First, both synergies and trade-offs were present. For example, SDG 15 (life on land) synergized with SDG 8 in the case that tourism contributed management funds (SDG 15 target 15.2) in Sagarmatha National Park of Nepal and Serengeti National Park in Tanzania; while it traded off with SDG 8 in other cases, including landscape fragmented and biodiversity decreased (SDG target 15.5) due to tourism in Zhangjiajie National Forest Park in China (Table 1). Second, only synergies were present. For example, tourism strengthened multi-stakeholder partnerships (SDG 17) in Dartmoor National Park of the United Kingdom, Zhangjiajie National Forest Park, and Kruger National Park in South Africa; increased income (SDG 2) and improved infrastructures (SDG 9) in Zhangjiajie National Forest Park; and increased management fund (SDG 14) for the Great Barrier Reef Marine Park in Australia (Table 1). Third, only trade-offs were identified. For example, large tourism volumes (over 900,000 visitors per year) in Machu Picchu of Peru resulted in limited access for indigenous peoples and degradation of the site (SDG 12.b) (Table 1).
First, both synergies and trade-offs occurred. For example, SDG 15 (life on land) synergized with SDG 8 (decent work and economic growth) in one case: tourism revenue of Serengeti National Park provided management funds to 11 additional less-visited national parks in Tanzania; while SDG 15 traded off with SDG 8in another case: large numbers of tourists had negative ecological effects in the surrounding area of Machu Picchu (Table 1). Another example is the relationships between SDG 1 (no poverty) and SDG 8 (decent work and economic growth): a synergy occurred when neighboring communities benefited from tourism in Kruger National Park, within which 4.1% of the local population lifted above the absolute poverty line of $1.25 per day (SDG target 1.1); a trade-off occurred when local communities bordering the Serengeti National Park faced exacerbated poverty due to limited tourism benefits, reduced land availability, and increased wildlife conflicts (Table 1). Second, only synergies were identified. For example, tourism in five protected areas (Dartmoor National Park, Zhangjiajie National Forest Park, Kruger National Park, Great Barrier Reef Marine Park, and Sagarmatha National Park) enhanced multi-stakeholder partnerships (SDG 17) of the neighboring areas of these protected areas (Table 1).
3.1.3. Within SDG 8 (Decent Work and Economic Growth), both Synergies and Trade-Offs Existed within Focal Systems as well as across Spillover Systems Within the focal system, Zhangjiajie National Forest Park, one trade-off was observed between SDG target 8.9 (tourism) and 8.4.1 (material footprint, material footprint per capita, and material footprint per GDP) (Table 1). Across spillover systems, tourism in 11 of the 12 protected areas (except for Kruger National Park) has promoted the tourism economy of neighboring towns, villages, cities, or the whole country (Table 1).
Regarding the 10 cases of wildlife translocations (Figure 3 and Table 2), we found that beyond the directly related SDG 15 (life on land), wildlife translocations enhanced or compromised SDGs 1 (no poverty) and 3 (good health and well-being) in focal systems, and SDGs 2 (zero hunger) and 3 in spillover systems. One synergy occurred between sustainable use of wildlife (SDG target 15.1) and investment in poverty eradication actions (SDG target 1.b) in the trade case of African lions (Panthera leo), because the main benefiting provinces (North West, Limpopo, and Free State) are among the poorest in South Africa, and the economic benefits from lion breeding and hunting industry are significant (Table 2). Trade-off occurred when raccoons were translocated from Florida to West Virginia to augment its local raccoon population (SDG target 15.8), which brought about rabies epizootic (SDG target 3.3) in the local raccoon population (Table 2).
3.2.2. Across Spillover Systems, Only Trade-Offs between SDG 15 (Life on Land) and SDGs 2 (Zero Hunger) and 3 (Good Health and Well-Being) Were Observed Regarding SDG 2, in 1952, 263 domestic reindeer (Rangifer tarandus) were traded and translocated from Norway to the Inuit community in Godthåbsfjord of western Greenland to provide a new livelihood (SDG target 2.3). This translocation brought two invasive parasitic insects (SDG target 15.8), warble fly (Oedemagna tarandi) and nostril fly (Cephenemyia trompe), which infested all indigenous wild Greenland caribou (R. tarandus groenlandicus) in the whole western Greenland (spillover system) (Table 2). The translocated raccoons from Florida to West Virginia to augment the local raccoon population (SDG target 15.8) also brought rabies epizootic (SDG target 3.3) to raccoons and skunks (Mephitis mephitis) in Pennsylvania, Virginia, and Maryland, which was a trade-off between SDGs 15 and 3 across the spillover systems (Table 2). 2). Across spillover systems, synergies occurred when the trade policy of grey and Timneh parrots in the United States and the trade policy of caiman in Venezuela led to positive effects in the spillover systems (Table 2). Trade-offs existed in more cases (Table 2). The restriction of wildlife trade in focal systems sometimes increased the trade in other places (spillover systems), for example, prohibited polar bear importations to the United States increased the importation to EU, Russia, and Canada; suspensions of reticulated python (Python reticulatus) trade from Indonesia promoted the trade from Singapore; banned export of its endemic parrots in Australia led to the laundering of wild-caught native Australian species in New Zealand and transporting them "legally" out of New Zealand to other countries. Translocated animals sometimes become invasive species. For example, the introduced American grey squirrels (Sciurus carolinensis) in Stupinigi, Italy, and North American beavers (Castor canadensis) in Lake Fagnano, Argentina, for hunting or fur-trapping purposes posed negative effects to neighboring ecosystems (Table 2).
The United States (receiving system) had been a major importer of wild-sourced Psittacus parrots and accounted for 47% of annual imports of more than 50,000 parrots (mainly from the west and central African countries) before the 1992 Wild Bird Conservation Act passed. The import restriction of wild birds in the United States accelerated the expansion of the captive-breeding industry in South Africa (spillover system), which had over 1600 breeding facilities by 2015 and accounted for 67% of all captive-bred exports. This synergy between the focal system (United States) and spillover system (South Africa) was within SDG 15, however, it generated trade-off between South Africa and the Democratic Republic of the Congo (DRC), because South Africa was a major importer of wild-sourced Psittacus parrots (used as breeding stock) from the DRC, from where 92% of 42,965 wild-sourced Psittacus parrots were exported during 2006-2014. In this way, the enhanced SDG 15 in one spillover system (South Africa) compromised SDG 15 in another spillover system (DRC) due to the efforts toward SDG 15 in the focal system (United States) (Table 2).
We applied the framework [6], which explicitly tackles increasingly important cross-boundary interactions in the context of SDG interactions, to guide the synthesis of tourism and wildlife translocations cases from the six continents and analyzed SDG synergies and trade-offs among 10 SDGs. Besides the SDG synergies and trade-offs within a system boundary, by tracking the effects of the flows, both direct and indirect SDG synergies and trade-offs across spillover systems were identified. Our results suggest that the SDG interactions within and across boundaries are widespread because of the extensive metacoupled flows.
Tourism and wildlife translocations are two types of globally common and important flows, and share features with other metacoupled processes, such as international trade [17,19], ecosystem services [81,82], and migration [83], as many studies indicate [26,34,84,85]. For instance, various flows connect sending, receiving, and spillover systems, which are affected socioeconomically and environmentally by the flows. The identification of widespread spillover systems is a new frontier in sustainability research [21,35]. It is a similarity that both tourists and wildlife flows impact spillover systems, however, the spillover effects affected SDGs at different spatial scales. Our study suggests that the spillover systems are distributed from neighboring areas to several distant countries (Figure 3). The composition of spillover systems differs for different cross-boundary processes. For instance, for tourism, the spillover effects mainly occur in adjacent areas, while the implementation of wildlife translocations could impact far away spillover systems, such as remote countries (Figure 3). The variation may also depend on the magnitude of flows, for example, a large number of tourists may influence larger neighboring areas. Besides the spillover systems and spatial scales, tourists and wildlife flows have similarities and differences in the influenced SDGs. Our synthesis indicates that both synergies and trade-offs existed within the same SDG (e.g., SDG 8 for tourism and SDG 15 for wildlife translocations) within focal systems as well as across spillover systems. Across the social, economic, and environmental dimensions of sustainable development, the tourist flow to protected areas influenced both SDGs of social inclusion (SDGs 1 and 2) and environmental sustainability (SDGs 14 and 15), besides the economic SDGs (8 and 9). As for wildlife translocations, our synthesis only identified synergies and trade-offs between environmental SDGs (15) and social inclusion SDGs (1, 2, and 3).
The World Tourism Organization claimed that tourism has the potential to contribute to all of the 17 SDGs [36]. From only 12 protected areas, our synthesis (Figure 3 and Table 1) indicates that, besides the directly related SDG 8 and target 8.9, tourism flow influenced seven other SDGs (1, 2, 9, 12, 14, 15, and 17) in the protected areas and across spillover systems. For many cases, the fact that SDG interactions were not identified is more likely to reflect data deficiencies than the absence of SDG synergies and trade-offs. It will be possible to identify more SDG interactions by tracking other positive or negative effects of the tourists flows and other associated flows (e.g., money). For example, the increased waste and sewage from tourism reduce water quality [86] may lead to trade-offs with SDG 6 (clean water and sanitation) both locally and across adjacent areas, and carbon emissions due to tourism [87] may influence spillover systems at the global scale and result in a trade-off with SDG 13 (climate action). Identifying, tracking, and quantifying the flows, spillover systems, and effects on SDG interactions need more investigation.
Regarding the wildlife translocations (Figure 3 and Table 2), besides the directly related SDG 15 (life on land), we identified relatively fewer SDGs (1, 2, and 3) that were enhanced or compromised in the focal systems and across spillover systems. Similar to the tourism cases, it is possible to explore more impacted SDGs and interactions among them by tracking the flows. For example, the panda loans favored multiple SDGs (9, 15, and 17) within Wolong and across spillover systems [6], while the associated carbon emissions may also lead to trade-offs with SDG 13 (climate change) [84]. Moreover, similar to the raccoon translocation program (Table 2), the wildlife trade has noticeably played a role in the emergence of zoonotic disease (SDG 3) across the world [43], such as monkeypox in the USA [88,89], Ebola in Africa, salmonellosis in the USA and Europe [90]. The most recent case is the novel coronavirus COVID-19, which was believed bat trade-related [44], and has spread on a global scale, and resulted in over 23 million people infected and more than 800,000 people died as of 23 August 2020 [45]. The affected spillover systems go beyond the better connected urban areas to smaller cities, towns, and rural areas globally through trade and travel [91]. Beyond public health (SDG 3), the COVID-19 may have impacts on all of the 17 SDGs. For example, the ongoing pandemic, the lockdown, and the travel restriction around the world have imposed negative impacts on poverty alleviation (SDG 1) [92], food security (SDG 2) (availability, access, utilization, and stability) [91,93], quality education (SDG 4) [94], gender equality (SDG 5) [94], sustainable energy (SDG 7) [95], and reducing inequality (SDG 10) [92], the tourism industry [96,97], and the world economy [98] (SDG 8). Moreover, although the reduced economic activities have resulted in some unexpected profit of climate change mitigation (SDG 13) [99], improved water quality in some areas (SDG 6) [100], and improved air quality in some urban areas (SDG 11) [101,102], negative environmental impacts such as increased pharmaceutical and household waste and consumption (SDG 12) increase challenges in management [103]. It is more essential than ever to govern the cross-boundary flows, such as improving the biosecurity of the wildlife trade [90], to reduce the trade-offs and promote the synergies of achieving SDGs.
Although our results have illustrated that the SDG interactions within and across boundaries are widespread due to extensive metacoupled tourism and wildlife translocations, we note there were limitations in this study. First, more data are needed to understand the reasons behind the conclusion that more synergies than tradeoffs occurred-our synthesis identified 33 synergies and 14 trade-offs among 23 targets of 10 SDGs. Future studies need to track the flows to identify the spillover systems and evaluate socioeconomic and environmental effects. Moreover, the 22 cases were relatively small compared to the large amount of relevant literature. Our synthesis showed that the SDG interactions across spillover systems are geographically widespread, although they may not sufficiently represent the global tourism and wildlife translocations, because these are only a small proportion of the enormous tourism in protected areas (e.g., at least 8 billion annual visits [39]) and the international trade of more than 5000 animal species and 29,000 plant species [40].
Despite the above limitations, our synthesis suggests that, beyond the flows of tourism and wildlife translocation and these specific cases, special attention should be paid to the increasingly important metacoupled flows that affect SDG synergies and trade-offs across boundaries. Theoretically, the synthesis showed that the general framework [6] is a useful tool and can provide general guidance for quantifying SDG interactions across different locations around the world in future studies. Practically, attempts to accomplish the 17 SDGs everywhere should include extensive spillover systems in sustainability governance [35]. Moreover, to hinder trade-offs and enhance synergies among SDGs across boundaries, it is necessary to track, evaluate, and manage the flows. We hope that this study will provide insights for more empirical investigation on SDG interactions that integrate local, regional, and global flows (of people, money, matter, and information) to enhance holistic management to achieve the 2030 Agenda locally to globally.
Sustainability 2020, 12, 7677; doi:10.3390/su12187677 www.mdpi.com/journal/sustainability