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This research focuses on addressing methodological issues that impact the performance of spatially explicit discrete choice agent-based land use models that are estimated with remotely sensed data. The empirical setting considers land use transitions between agroforests, perennial crops, grass and corn, and fallow lands during the period 1984 - 2006 in a Mexican coffee growing region in which relatively high deforestation rates were observed. As a starting point, a Mixed Conditional - Multinomial Logit model is implemented to highlight assumptions and limitations associated with this standard modeling approach. The results indicate that this model produces theoretically inconsistent parameter estimates for the revenue variable associated with three out of four land uses considered in the analysis. To investigate whether those counterintuitive marginal effects are generated from misclassified land use data, a Latent Multinomial Logit (LMNL) model is implemented. This approach allows the identification of land use observations that have a high likelihood of being wrongly classified. A reconfiguration of the dataset based on the LMNL model increased the magnitudes of the marginal effects of the analyzed land use drivers in the theoretically expected directions. Next, because static land use models require limiting assumptions that potentially oversimplify the behavioral process followed by landowners, a structural dynamic discrete choice model of land use decisions is implemented under the assumption that land managers are forward-looking and act to maximize their discounted flow of current and future expected utility within a stochastic environment. A comparison between static and dynamic models shows that the directions of the marginal effects corresponding to time-invariant parcel-specific variables generally have the expected directions independent of the selected modeling approach. More importantly, the marginal effect estimates for the revenue variables of the agroforestry and perennial crops categories have the expected direction in the dynamic model. By contrast the myopic modeling approaches generate counter-intuitive results for the revenue variable that corresponds to perennial crops production, which affects the validity of those results for policy design. Finally, a policy simulation exercise shows the sensitivity of welfare estimates to the discount factor selected as representative of the true value used by decision makers.

The increasing severity and frequency of environmental climate-driven forest disturbances challenge the sustainable management and protection of forests in the United States (U.S.), and the benefits they provide to individuals and communities. Between 1985 and 2015, about 9 million acres of U.S. forest land was disturbed annually across the country. In the western U.S., wildfires, droughts, timber harvesting, and bark beetle outbreaks are the main drivers of tree mortality, loss of recreation opportunities, and water quantity. An understanding of the environmental and socioeconomic consequences of forest disturbances could improve the management and protection of public lands.This study provides monetized estimates of forest disturbances for two key areas of value: recreation and carbon storage. We estimate the monetary value of these ecosystem services and how they are affected by fire, drought or disease due to climate change. We employ benefit function transfer to estimate the economic value of forest ecosystem services u2013 both market and non-market values. This information may improve stakeholdersu2019 understanding of how heterogeneous forests disturbances affect economic and environmental processes at different temporal and spatial scales. The results could also inform the design of spatially targeted forest conservation, pest control, or fire suppression strategies to generate greater benefits to society.

This study presents the first application of deep learning techniques in capturing long-term, time-continuous forest cover dynamics at a continental scale. We developed a spatially-explicit ensemble model for projecting Australia's forest cover change using Long Short-Term Memory (LSTM) deep learning neural networks applied toa multi-dimensional, high-resolution spatiotemporal dataset and run on a high-performance computing cluster.We further quantified the influence of explanatory variables on the spatiotemporal dynamics of continental forest cover. Deep learning greatly outperformed a state-of-the-art spatial-econometric model at continental,state, and grid-cell scales. For example, at the continental scale, compared to the spatial-econometric model, the deep learning model improved projection performance by 44% (root-mean-square error) and 12% (pseudo R squared).The results illustrate the robustness and effectiveness of the LSTM model. This work provides a reliable tool for projecting forest cover and agricultural production under given future scenarios, supporting decision-making in sustainable land development, management, and conservation. Refereed/Peer-reviewed

The restoration and protection of “blue carbon” ecosystems – mangroves, seagrasses, and tidal marshes – has potential to offset greenhouse gas emissions and improve coastal livelihoods. However, realisation of this potential relies on global investment in restoration and protection, which in turn relies on appropriate funding mechanisms that are currently impeded by multiple constraints. Constraints include commercial considerations by private investors (including reliable estimates of financial returns, risk quantification and management, and supply chain impacts), regulatory and legal uncertainty (such as the complexity of property rights in coastal areas, policy coordination across jurisdictions, and stable policy consistent with the duration of blue carbon projects). There are, however, opportunities to improve on current practices, including better stakeholder engagement (including social license to operate, and knowledge transfer to promote best practices), and targeted use of public and philanthropic funding (to subsidise demonstration projects, reduce financial risk through collaterals, and promote low-profit but high co-benefit projects). In this paper, a strategy for the realisation of potential benefits through commercially viable and scientifically robust blue carbon initiatives is presented alongside insights and guidance for the policy, research, civil society, and private sectors to achieve these important long-term outcomes Refereed/Peer-reviewed

Recent rates of deforestation on private lands in Australia rival deforestation hotspots around the world, despite conservation policies in place to avert deforestation. This study uses causal impact estimation techniques to determine if a controversial conservation policy-the Vegetation Management Act (VMA)-has successfully reduced deforestation of remnant trees in the Brigalow Belt South, a 21.6 Mha biodiversity hotspot in Queensland. Weuse covariate matching to determine the regulatory effect of the policy on deforestation rates over time, compared to two counterfactual scenarios representing upper and lower estimates of policy impact. The VMA significantly reduced the rate of remnant deforestation in the highest impact scenario, saving 17, 729 +/- 1733 ha during 2000-2016. In the lowest scenario, 'panic clearing' before and after enactment of the VMA minimized the amount of remnant forests saved and may have marginally increased deforestation relative to the counterfactual (-404 +/- 617 ha). At peak effectiveness, the VMA successfully counteracted the amount of remnant deforestation during 2010-2012, but this only represents 4.78% of the 371, 252 ha of remnant forests cleared in the bioregion since enactment in 1999. Thus, while deforestation rates in the region have substantially reduced since the policy was enacted, our results of positive yet limited direct regulatory impact suggests the policy's effectiveness is strongly confounded by other deforestation drivers, like changing socio-economic or climate conditions, as well as new social signals provoked by the policy. The mechanisms through which the policy influences deforestation behavior must be further investigated to ensure real, desirable change is achieved. Refereed/Peer-reviewed

Environmental policies and regulations have been instrumental in influencing deforestation rates around the world. Understanding how these policies change stakeholder behaviours is critical for determining policy impact. In Queensland, Australia, changes in native vegetation management policy seem to have influenced land clearing behaviour of landholders. Periods of peak clearing rates have been associated with periods preceding the introduction of stricter legislation. However, the characteristics of clearing patterns during the last two decades are poorly understood. This study investigates the underlying spatiotemporal patterns in land clearing using a range of biophysical, climatic, and property characteristics of clearing events. Principal component and hierarchical cluster analyses were applied to identify dissimilarities between years along the political timeline. Overall, aggregate landholders' clearing characteristics remain generally consistent over time, though noticeable deviations are observed at smaller regional and temporal scales. While clearing patterns in some regions have shifted to reflect the policy's goals, others have experienced minimal or contradictory changes following regulation. Potential 'panic' or 'pre-emptive' effects are evident in the analysis, such as spikes in clearing for pasture expansions, but differ across regions. Because different regions are driven by different pressures, such as land availability and regulatory opportunity, it is imperative that the varying spatial and temporal behavioural responses of landholders are monitored to understand the influence of policy and its evolution. Future policy amendments would benefit from monitoring these regional responses from landholder s to better assess the effectiveness of policy and the potential perversities of policy uncertainty. Refereed/Peer-reviewed

The shift from net forest loss to gain—forest transition—has been associated variously with economic development, market-driven reforestation, forest policy, and globalization. Evidence shows that governments can expedite forest transition, although economic and institutional failures can distort policy incentives. This study addresses the paucity of spatially explicit empirical research on the robustness of the forest transition hypothesis in a developed country context and identifies factors that may hasten, delay, or even reverse forest transition. We applied spatial-econometric analysis to high-resolution forest cover, climatic, socioeconomic, physiographic, and State-jurisdiction data for the Australian intensive agricultural zone from 1988 to 2014. While environmental and physiographic factors explained the spatial distribution of forests, net forest cover change was significantly associated with trends in farm-output prices inducing deforestation in Queensland, the State with less effective land clearance regulations. Changes in land clearing regulations in Queensland were significantly associated with the national forest cover trends that resulted in forest transition in Australia around 2008. Yet when land clearing regulations and their enforcement were subsequently relaxed in 2012, significant forest cover loss was once again observed in that State, particularly in remnant forests. We conclude that if forest regulatory protection is not effective, net forest loss could resume or increase, even in developed countries, in response to growing incentives for forest conversion to agriculture. Refereed/Peer-reviewed

A detailed understanding of multiple human and environmental factors influencing land allocations among agricultural uses can facilitate more efficient and targeted land policy. To show this, we used a comprehensive dataset of socioeconomic, physiographic, and climatic indicators to investigate potential determinants of land-use in Australia's intensive agricultural region during the period 1992–2010. We applied a seemingly unrelated regressions land-use shares spatial error model with random effects coupled with variance decomposition analysis to identify the statistical significance, direction and magnitude of observed associations between land-use and its drivers. Population: density, rainfall, equity ratio, and access to markets were the most influential policy-relevant land-use factors. Land allocations to cereals and livestock production were significantly influenced by spatiotemporal rainfall and temperature variability. Improved pastures, cereals, annual and perennial crops plantations were larger in regions with better access to markets. Increases in equity ratio (i.e., better financial position) were associated with larger land allocations to improved pastures and annual crops and smaller extensive grazing area. Marginal associations were detected between land-use and output prices, and higher population density was associated with lower shares for all high value agricultural land-uses. The results suggest that improved transportation infrastructure, zoning regulations, and mechanisms to reduce farm debt exposure and risks from climate variability could have significant impact on the configuration of the Australian agricultural landscape. Refereed/Peer-reviewed