Telecoupling as a framework to support a more nuanced understanding of causality in land system science

Figures & data

Table 1. Terms related to the complexity of attributing causality in telecoupled land-use systems.

Term	Definition	Source
Causes
Multi-causality	Any given pattern may be caused by several different processes, and the action of each is dependent on context.	Chapman et al., 2017; Lambin et al., 2001
Confounders	A variable that influences explanatory (independent) and response (dependent) variables. Confounding variables can be both observable and unobservable.	Carlson et al. (2018)
Proximate (or direct) causes	Human activities or immediate actions at the local level that originate from the observed change and directly impact the observed change.	Geist & Lambin, 2002; Meyfroidt, 2016
Underlying (or indirect) causes	Fundamental forces that underpin the more proximate causes.	Geist & Lambin, 2002; Meyfroidt, 2016
Effects
Cascading effect	The process by which a system affects other multiple systems in sequence as a result of telecoupling dynamics; occurs when a change of one element of a system drives a chain of events leading to many other changes in the system.	Baird & Fox, 2015; Paitan & Verburg, 2019
Cumulative effect	impact of the action when added to other past, present, and reasonably foreseeable future actions regardless of what agency […] or person undertakes such other actions.	Clark, 1994
Legacy effect	Effects that do not disappear until many years to decades after the emergence of a telecoupling.	Liu, 2014; Norder et al., 2017; Paitan & Verburg, 2019
Non-linearity	Social and ecological patterns do not gradually change as a linear function of relevant processes but rather display thresholds, time lags, and generally complex behavior (including regime shifts).	Chapman et al., 2017; Paitan & Verburg, 2019
Threshold effects	Seemingly stable systems can suddenly undergo comprehensive transformations into something entirely new, with internal controls and characteristics that are profoundly different from those of the original. Small events might trigger changes that are difficult or even impossible to reverse.	Duit & Galaz, 2008
Time lags (or inertia)	Effects that do not emerge until years or even decades after the initiation of a telecoupling.	Liu, 2014; Norder et al., 2017; Paitan & Verburg, 2019
Multifinality	Similar combinations of causal factors result in substantially different outcomes, for example, due to small variations in contextual factors or contingent events.	Bennet and Elman 2006; Meyfroidt, 2016
Equifinality	Different combinations of causes that end up in similar outcome.	Bennet and Elman 2006; Meyfroidt, 2016
Temporal spillover	Conducting behavior A in time 1 affects the probability of conducting behavior A in time 2.	Nilsson et al., 2017
Cause and effect
Feedback	Feedbacks occur between systems when effects of the first system on a second system feed back to affect the first system. Feedbacks can be negative (damping) or positive (amplifying).	Liu et al., 2013; Rotmans & Loorbach, 2009
Feedback loop	Feedback loops, or reverse causality, can be related to bidirectional causation and implies that events in a nonlinear causal chain can be both causes and effects,	(Carlson et al., ; Meyfroidt, 2016; Sugihara et al., 2012)
Multi-scalarity	Relevant processes are simultaneously operating at a diversity of scales, manifesting in patterns at multiple scales (both temporal and spatial).	Chapman et al., 2017
Path dependence	Positive feedback loops or self-reinforcing sequences i.e. chains of chronologically ordered and causally connected events which are more tightly connected and less contingent than in typical causal chains.	Mahoney, 2000; Pierson, 2000
Unobserved heterogeneity	The unmeasured third factors that may affect the relation between the causal factor and the outcome.	Meyfroidt, 2016
Spatial decoupling	The decoupling of drivers and outcomes which gives rise to telecouplings.	Friis et al., 2016

Figure 1. PRISMA of literature included for review. Source: Figure generated from own data in http://prisma.thetacollaborative.ca/.

Figure 2. Type of tools applied to justify causal statements. Source: Figure generated from own data.

Figure 3. Data source within qualitative and quantitative studies. Source: Figure generated from own data.

Figure 4. Telecoupling application and analytical focus. Source: Figure generated from own data.

Figure 5. Reported contributions of the telecoupling framework. Source: Figure generated from own data.

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Data availability statement

Raw data were generated in NVivo. Derived data supporting the findings of this study are available from the corresponding author upon request.