Public policy for biodiversity conservation: evaluating outcomes, opportunities and risks

Date

2017

Authors

Evans, Megan Catherine

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

The conservation of biodiversity is a daunting and complex public policy challenge. Over the past three decades, two clear themes have emerged in conservation science, policy and practice: greater experimentation with market-based policy instruments (MBIs); and an increased concern over the effectiveness of conservation policies. These two themes are interrelated, as a key driver of the rise in prominence of MBIs has been the promise of more effective, efficient and equitable conservation than that which is possible under ‘traditional’ regulatory approaches. However, scarce evidence is available on the efficacy of regulatory policies and MBIs alike, and it has been argued that “better theory, better methods, and better data” are required if conservation policies are to be more frequently and rigorously evaluated for effectiveness. This focus on the technical challenges of policy evaluation is incomplete, as effectiveness of conservation policy is influenced not only by the choice of policy instrument or combination thereof, but also the actors involved, the relevant institutional, social and political contexts, and decisions made at various stages of the policy process. In this thesis, I investigate the challenges and complexities associated with conservation policy in Australia, an advanced and politically stable economy. Using an interdisciplinary, mixed methods approach, I consider regulatory and market-based policy responses to a major driver of biodiversity loss, deforestation, and evaluate what outcomes, opportunities and risks these policies present for conservation. In Chapter Two, I document the recent shift away from ‘command and control’ policy responses to deforestation in Australia, and towards self-regulation and MBIs. Despite this change in policy style, little is known of their efficacy. In Chapter Three, I use a spatially explicit bent-cable regression model to evaluate what effect regulatory policies have had on the rate of deforestation in Queensland, Australia. I find some evidence of a policy effect after adjusting for covariates, but extreme variation in regional deforestation trends reduces this effect at the state level. In Chapter Four, I present findings which confirm that carbon farming is economically viable in degraded Queensland agricultural landscapes under an estimated $5 t CO2e-1 carbon price. In practice however, large-scale reforestation has not occurred despite being the ‘rational’ option, in part due to policy complexity and political uncertainty. In the final three empirical chapters, I consider challenges in the design, implementation and evaluation of biodiversity offset policy. In Chapter Five I describe a mathematical framework used to underpin the Australian Environmental Offsets Policy, which was designed to deliver ‘no net loss’ outcomes for protected matters. I subsequently illustrate in Chapters Six and Seven that improvements to policy design do not necessarily lead to better policy outcomes, due to complexities that emerge through policy implementation in the context of multi-actor, multi-level environmental governance. I draw on qualitative data from interviews with key informants to describe potential risks to biodiversity outcomes under current offset policy settings, including: ambiguous responsibility for long term security and management, fragmentation within government departments at the federal and state levels, and a lack of transparency and public accountability. I conclude the thesis and provide future research directions in Chapter Eight.

Description

Keywords

Deforestation, evaluation, public policy, biodiversity conservation, environmental governance, market based instruments, environmental regulation, mixed methods, interdisciplinary research

Citation

Source

Type

Thesis (PhD)

Book Title

Entity type

Access Statement

License Rights

Restricted until

Downloads