2018
DOI: 10.1016/j.envsci.2018.09.011
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Frequent policy uncertainty can negate the benefits of forest conservation policy

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Cited by 37 publications
(29 citation statements)
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“…The ATTs for this pre-intervention period revealed remnant deforestation rates significantly higher than the counterfactual (ATT 1999,FO =4.46%, ATT 1999,FL =3.29%). Two explanations for this preferential clearing of remnant trees compared to similar non-remnant trees in this period are: (1) that this period shows 'pre-emptive' clearing of remnant trees prior to the Act, evidence of which has been reported in previous analyses (Simmons et al 2018a(Simmons et al , 2018b and landholder testimonials (Productivity Commission 2004, Senate Inquiry 2010), or (2) that this is a social preference for clearing of remnant trees versus similar areas of non-remnant trees. We developed two assumption scenarios from these: (1) Pre-emptive Clearing scenario (ATT PC ), assuming that elevated pre-emptive clearing of remnant trees is caused entirely by the Act and that the true difference between our treated and counterfactual samples is zero (ATT 1999 =0%), and (2) Social Preference scenario (ATT SP ), assuming that there is a fixed social preference for clearing remnant over equivalent nonremnant, which is equal to a constant deforestation rate of SP FO =4.46% for 2000-2004 and SP FL = 3.29% for 2005-2016.…”
Section: Scenarios and Causal Impact Estimationmentioning
confidence: 69%
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“…The ATTs for this pre-intervention period revealed remnant deforestation rates significantly higher than the counterfactual (ATT 1999,FO =4.46%, ATT 1999,FL =3.29%). Two explanations for this preferential clearing of remnant trees compared to similar non-remnant trees in this period are: (1) that this period shows 'pre-emptive' clearing of remnant trees prior to the Act, evidence of which has been reported in previous analyses (Simmons et al 2018a(Simmons et al , 2018b and landholder testimonials (Productivity Commission 2004, Senate Inquiry 2010), or (2) that this is a social preference for clearing of remnant trees versus similar areas of non-remnant trees. We developed two assumption scenarios from these: (1) Pre-emptive Clearing scenario (ATT PC ), assuming that elevated pre-emptive clearing of remnant trees is caused entirely by the Act and that the true difference between our treated and counterfactual samples is zero (ATT 1999 =0%), and (2) Social Preference scenario (ATT SP ), assuming that there is a fixed social preference for clearing remnant over equivalent nonremnant, which is equal to a constant deforestation rate of SP FO =4.46% for 2000-2004 and SP FL = 3.29% for 2005-2016.…”
Section: Scenarios and Causal Impact Estimationmentioning
confidence: 69%
“…The BBS experienced the greatest rainfall deficit of any bioregion in Queensland during Australia's 'Millennium Drought' of 2001(Van Dijk et al 2013, and annual rainfall patterns have been identified as a significant driver of remnant and non-remnant deforestation in the region (Simmons et al 2018b). It is likely that this drought was largely responsible for the reduction of absolute deforestation rates during this period by diminishing the economic incentives to clear, which could explain why the VMA was only marginally effective in those years.…”
Section: Complementing Trend Impact Indicatorsmentioning
confidence: 99%
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“…Simmons et al 2018c, Chapter 3), which could explain the sensitivity of impact estimates during this period. For these years, other unobserved confounders such as additional socioeconomic factors, aesthetic values of nature, or shifts in social or cultural norms could also have a significant effect on the impact of the VMA(Kull et al 2007;Seabrook et al 2008;Marcos-Martinez et al 2017).…”
mentioning
confidence: 99%
“…The BBS experienced the greatest rainfall deficit of any bioregion in Queensland during Australia's 'Millennium Drought' of 2001-2009 (van Dijk et al 2013), and annual rainfall patterns have been identified as a significant driver of remnant and non-remnant deforestation in the region (Simmons et al 2018c,Chapter 3). It is likely that this drought was largely responsible for the reduction of absolute deforestation rates during this period by diminishing the economic incentives to clear, which could explain why the VMA was only marginally effective in those years.Characteristics of landholders' clearing patterns during this period have reflected a preference for maximizing quality over quantity(Simmons et al 2018b, Chapter 2), and this may explain the subsequent increase in the quantity of non-remnant deforestation following the end of the drought.These climate restraints could also have flow-on effects on other socioeconomic drivers of deforestation, such as food prices and potential profitability, which can ultimately diminish the direct impact of the VMA (Marcos-Martinez et al 2017;Rhodes et al 2017;Simmons et al 2018c, Chapter 3).…”
mentioning
confidence: 99%