Improving the Representation of Climate Change Adaptation Behaviour in New Zealand’s Forest Growing Sector
Abstract
:1. Introduction
The Need for Modelling Alternatives in the Forestry Sector
2. Modelling Climate Change Adaptation and Theoretical Representations
Behavioural Type | Decision | |||||
---|---|---|---|---|---|---|
Theory | Description | Assumption | Applicability/Example | Strength | Weakness | |
Forward looking | Rational 1 decision makers (Homo economicus) | Expected Utility Theory is a theory of choice under risk and uncertainty where the decision maker chooses the option that promises the highest expected utility [29,41]. | Actors have perfect and complete knowledge and unlimited computational processing powers. Decision making is goal oriented with stable preferences The rational decision makers maximise their utility or profit (Maximiser). | The socio-economic agent model focuses on the selling (supply) and buying (demand) of timber, which later influences the forest succession process [42]. | Easy to link to forest growth models based on costs and benefits. Includes: adaptation costs, risk perception, time-preferences and income constraints [21]. Many applications. | Does not include other psychological factors, such as perceived ability to perform, and subjective norms and attitudes [21]. No forest owners and managers are perfectly rational profit maximisers. |
Behavioural economics | Prospect theory. The theory describes how individuals generally overweigh low-probability/high-impact events and underweigh high-probability/low-impact events [35,43]. | Human agents are influenced by psychological biases such as endowment effect (i.e., agents derive utility not from wealth, but from gains and losses defined to some reference level), loss aversion (i.e., a loss hurts more than an equally large gain produces joy). | Using the extension of Smooth Prospect theory (SPT), artificial market agents were simulated against traditional agents (based on EUT) using ABM. The results showed that agents based on SPT demonstrated behaviours that were closer to real market data in risky environments [29]. | Accounts for loss aversion, bounded rationality in evaluation, risk perception, adaptation costs, time-preference, and income constraints [21]. | Does not include other psychological factors, such as perceived ability to perform, and subjective norms and attitudes [21]. Suitable decision rules highly context-dependent [27]. | |
Backward looking | Cognitive/Psychological (Homo psychologicus) | Learning theory focuses on the past, where an agent or actor learns that a certain action leads to a reward that feels good or satisfying, and is therefore more likely to repeat this behaviour [27]. | Learning from experience and results of past actions. Learning occurs when the outcome of an agent’s decision to change or persist with its strategy matches its expectation of success. Decisions are also guided by rewards or punishments. | Agent considers experience of successful year including the historical information (e.g., fire event or pest outbreak) with various levels of risk acceptance [44]. | It can describe the adaptivity of agent behaviour to a changing environment (e.g., climate change) with limited information. | High degree of randomness in behavioural changes and local dynamics are often stylized [27]. Dos not specify how information is acquired and how beliefs are formed. |
Sideward looking | Social influence (Homo sociologicus) | Social learning theory (i.e., adaptive management approach) The attitudes and decisions of one agent connected to another agent influence agents’ attitudes or decisions. Agent learns from its interactions with nonspecific agents. This results in a change in understanding that goes beyond the individual, situating them within wider groups in society [45]. | Assumes that successful intervention as a learned process depends on the appropriate communication channels. Social influence is exerted when the agent cannot reach his/her own decision and thus imitates the behaviour of the majority [46]. | Scenario-based landscape planning (participatory planning process on climate change adaptation). The FLAME model has the agent’s decision constrained by the opinions of other agents with whom they have communicated [47]. | Specifies how the information is acquired and beliefs are formed by individuals. Explains the formation of consensus, the emergence of clustered opinion distributions and polarization. | High degree of randomness in behavioural changes and local dynamics are often stylized [27]. |
Social network theory focuses on the role of social relationships in transmitting information, channelling personal or media influence, and enabling attitudinal or behavioural change. | Assumes that social structure influences the behaviour, opinions, or beliefs of individual actors or agents, which in turn drives changes in social structure. Actors with similar characteristics tend to form new links between each other while breaking links with agents that have diverging characteristics. | Information received from others (e.g., social network), updates agents’ knowledge and updates their options according to their objectives. An analytical hierarchy process was applied to operationalise the agents’ behaviour in the context of the roundwood and wood fuel markets [48]. | ||||
Combination | Social/ Cognitive/ Psychological | Theory of planned behaviour focuses on intention as the main determinant or predictor of behaviour [49]. | Assumes that the stronger the intention to engage in a behaviour, the more likely its performance. Three factors affect the intention: (1) attitude toward the behaviour, which refers to the degree to which a person has a favourable or unfavourable assessment of the behaviour in question; (2) subjective norms, which refers to an individual’s perception about how significant others would judge the behaviour under consideration; and (3) perceived behavioural control, which refers to the perceived ease or difficulty of performing behaviour (reflecting past experience). | Applied to understanding forest owners’ timber stand improvement intention [50]. May be explored for planned adaptation in forest management Applied to explore the determinants of behaviours (recycling) through the development of the agent-based model; cognitive model of agent-based model was develop based on the result of the structural equation model [51]. | Includes individual attitudes and subjective norms [21]. | Does not include risk attitudes and time preferences [21]. The original theory does not provide a mathematical formalization [31]. |
Protection motivation theory (PMT) focuses on the conditions under which fear appeals may influence attitudes and behaviour [52,53]. | Assumes that fear and anxiety act as driving forces that motivate trial-and-error behaviour and decision-making towards adaptive practices. It assumes that various environmental (e.g., fear appeals) and intrapersonal sources of information can initiate two independent appraisal processes: (1) threat appraisal and (2) coping appraisal. | Climate change adaptation decisions in forestry. It explains and understands factors influencing climate change adaptation (and maladaptation) behaviour as well as crisis events due to environmental stresses [54]. Provides understanding as to the motivation for health and safety in forestry operations. | Combines risk perception and perceived costs and benefits of economic theories with individual coping perceptions [21,55]. Explains the subjective adaptive capacity of individuals (i.e., perceived self-efficacy). | Does not include a full distribution of risks and does not include risk attitudes and time preferences [21]. |
3. Integrating Climate Change Adaptation Decisions and Behaviour: Agent-Based Model Applications
- Embedding human and social behaviours and constraints within models, either through integrating agent-based models with process-based models or through structured approaches to constrain model input changes that reflect time-varying scenario-specific settings.
- Reflecting on the importance of extreme events in driving adaptation [72].
- Accounting for the full cost of adaptation, in terms of the type and the amount that can occur, thus reflecting the financial constraints on adaptation [33]. Existing forests are constrained by a high level of investment in existing crops and infrastructure, and by the long time frame before economic return, reducing the flexibility to adapt.
- Working with stakeholders and decision makers to better understand the triggers and goals of adaptation policies and measures.
4. Protection Motivation Theory (PMT): A Social–Psychological Framework
5. PMT Application to Adaptation of New Zealand’s Commercial Forestry
6. Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Aspect | Model | |
---|---|---|
Rammer and Seidl [4] | Blanco et al. [3] | |
Agent: | Forest managers/forest owners | Forest owners |
Interaction type: | Agent-emergent ecosystem dynamics. Based on the ecosystem information provided by the biophysical mode (i.e., iLand); silvicultural assessment is made for each stand at each time step (or year) | Agent—environment LPJ-GUESS ecosystem model—to simulate forest dynamics |
Agent behaviour/ decisions: | Two-tiered architecture:
* Decisions are based on key indicators: stand age, stocking level, species composition, and diameter distributions | Based on management roles/objectives and associated management preferences:
Management preferences are based on:
Competition—limited supply of land (using benefit function, which assigned value to production based on the societal demand level of each service) Forest rotation period |
Adaptation/ decisions: | Silvicultural decisions:
| Adaptation measure assumed to be known according to each management role (e.g., species composition, number of thinnings, rotation lengths) Coping ability is assessed using coping index, which reflects whether a management strategy is at least as competitive under an uncertain future global change scenario as under a reference scenario. |
Sub-models: | Simple decision heuristics | Gaussian probability distribution to represent individual differences in dedication to land use |
Scenario settings: | Passive vs. active adaptation | Combined representative concertation pathways and shared socio-economic pathways |
Programming: | Combined Javascript and C++ | Java Eclipse (http://crafty-abm.sourceforge.net/) (accessed on 28 September 2020). |
Risks | Climate Driver | Impacts | Adaptation | |
---|---|---|---|---|
Reactive/Strategic | Proactive/Transformational | |||
Change in growth productivity and wood quality | Increasing temperature Decreasing rainfallStrong winds | Productivity can be reduced from the indirect impacts from the increased risk of fire, pest, disease, and weed establishment and enhanced growth |
|
|
Pest and diseases outbreak | Increasing temperature Increasing rainfall | The climate changes can make forests more suitable for pests to establish viable populations |
|
|
Fire | Increasing temperature Decreasing rainfall | The productivity of understorey and the increase in dryness can increase fuel loads |
|
|
Erosion | Increasing rainfall and wind | Loss of established forests Loss of soil for pot-harvested stands |
|
|
Drought | Decreasing rainfall Increasing temperature | Impact can be significant for newly planted stands |
|
|
Wind throw and toppling | Increasing wind/storm incidence | Loss of productivity from stem-break damaged (non- utilisable) trees Some production loss from toppled trees, depending on the ability to quickly extract and process fallen trees. |
|
|
Survey Items | |||
---|---|---|---|
Response | Frequency | Percent | |
Temperature: expectation to change by 2050 | Decrease slightly | 8 | 1.6 |
No change | 63 | 13.1 | |
Increase slightly | 316 | 65.4 | |
Increase a lot | 57 | 11.8 | |
Unsure | 39 | 8.1 | |
Rainfall: expectation to change by 2050 | Decrease a lot | 9 | 1.8 |
Decrease slightly | 75 | 15.5 | |
No change | 125 | 25.8 | |
Increase slightly | 156 | 32.3 | |
Increase a lot | 50 | 10.4 | |
Unsure | 68 | 14.1 | |
Drought: prevalence to change by 2050 | Decrease a lot | 6 | 1.2 |
Decrease slightly | 33 | 6.8 | |
No change | 118 | 24.4 | |
Increase slightly | 190 | 39.4 | |
Increase a lot | 77 | 15.9 | |
Unsure | 59 | 12.2 | |
Adaptation strategy: | |||
Planted new land in forestry | Yes | 14 | 9.4 |
No | 134 | 90.5 | |
Change/replant different timber species | Yes | 8 | 5.4 |
No | 140 | 94.6 | |
New forest management practices (e.g., thinning) | Yes | 5 | 3.4 |
No | 143 | 96.6 | |
Convert to an uneven-aged forest | Yes | 4 | 2.7 |
No | 144 | 98.0 | |
Change rotation length: Harvest sooner | Yes | 3 | 2.0 |
No | 145 | 98.0 | |
Change rotation length: Harvest later | Yes | 4 | 2.7 |
No | 144 | 97.3 | |
Flooding concern: based on past experience | Yes | 168 | 34.8 |
No | 303 | 62.7 | |
Unsure | 12 | 2.4 | |
Flooding mitigation scheme: participation | Yes | 54 | 11.2 |
No | 405 | 83.8 | |
Unsure | 24 | 4.9 |
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Villamor, G.B.; Dunningham, A.; Stahlmann-Brown, P.; Clinton, P.W. Improving the Representation of Climate Change Adaptation Behaviour in New Zealand’s Forest Growing Sector. Land 2022, 11, 364. https://doi.org/10.3390/land11030364
Villamor GB, Dunningham A, Stahlmann-Brown P, Clinton PW. Improving the Representation of Climate Change Adaptation Behaviour in New Zealand’s Forest Growing Sector. Land. 2022; 11(3):364. https://doi.org/10.3390/land11030364
Chicago/Turabian StyleVillamor, Grace B., Andrew Dunningham, Philip Stahlmann-Brown, and Peter W. Clinton. 2022. "Improving the Representation of Climate Change Adaptation Behaviour in New Zealand’s Forest Growing Sector" Land 11, no. 3: 364. https://doi.org/10.3390/land11030364