Category Archives: Climate change

259 – Increasing environmental benefits

It is obvious that the budgets of our public environmental programs are small relative to the cost of fixing all of our environmental problems. If we want to achieve greater environmental benefits from our public investments, what, in broad terms, are the options?

I remember seeing a graph last year – I think it was from the Australian Bureau of Statistics – showing the level of concern felt by the Australian community about environmental issues. It looked to have peaked a few years ago, and was pretty flat, or slightly declining. In that context, the prospects for a big increase in environmental spending over time don’t look good, particularly given the general tightness of government budgets. So I was wondering, if we wanted to double the environmental values protected or enhanced by our public programs, what are the options? I was able to identify several. I’ll list them here, and briefly comment on their potential effectiveness, cost and political feasibility.

  1. Double the budget. Effectiveness: high (in the sense that we could actually double the environmental benefits generated). Cost: high. Politics: very unlikely in the foreseeable future. It wouldn’t be my first priority, anyway. Increasing the budget would be more effective if we first delivered some of the strategies below.
  2. Improve the prioritisation of environmental investments. Improve the usage of evidence, the quality of decision metrics (Pannell 2013), and the quality of evaluation of proposals. Effectiveness: high (because most programs currently have major deficiencies in these areas). Cost: low, especially relative to doubling the budget. Politics: Implies a higher degree of selectivity, which some stakeholders dislike. Probably means funding fewer, larger projects. Achievable for part of the budget but the politics probably require a proportion to be spent along traditional lines (relatively unprioritised).
  3. murray_riverEncourage more voluntary pro-environmental action through education, persuasion, peer pressure and the like. Effectiveness: commonly low, moderate in some cases. Cost: moderate. Politics: favourable.
  4. Increase the share of environmental funds invested in research and development to create pro-environmental technologies (Pannell 2009). Note that this is about creation of new technologies, rather than information. Examples could include more effective baits for feral cats, new types of trees that are commercially viable in areas threatened by dryland salinity, or new renewable energy technologies. Feasibility: case-specific – high in some cases, low in others. Cost: moderate. Politics: requires a degree of patience which can be politically problematic. Also may conflict with community desire to spend resources directly on on-ground works (even if the existing technologies are not suitable). There tends to be a preference for research funding to come from the research budget rather than the environment budget, although this likely means that it is not as well targeted to solve the most important environmental problems.
  5. Improve the design of environmental projects and programs. Improve evidence basis for identifying required actions. Improve selection of delivery mechanisms. Improve the logical consistency of projects. Effectiveness: high (because a lot of existing projects are not well founded on evidence, and/or don’t use appropriate delivery mechanisms, and/or are lacking in internal logical consistency). Cost: low. Politics: Implies changes in the way that projects are developed, with longer lead times, which may not be popular. There may be a perception of high transaction costs from this strategy (although they would be low relative to the benefits) (Pannell et al. 2013).
  6. Increase the emphasis on learning and using better information. Strategies include greater use of detailed feasibility studies, improved outcome-oriented monitoring, and active adaptive management. Effectiveness: moderate to high. Would feed into, and further improve, options 2 and 5. Cost: low. Politics: main barrier is political impatience, and a view that decisions based on judgement are sufficient even in the absence of good information. Often that view is supported/excused by an argument that action cannot and should not wait (which is a reasonable argument in certain cases, but usually is not).
  7. Reform inefficient and environmentally damaging policies and programs. Examples include subsidies for fossil fuels, badly designed policies supporting biofuels in Europe and in the USA, and agricultural subsidies. This strategy is quite unlike the other strategies discussed here, but it has enormous potential to generate environmental benefits in countries that have these types of policies. Successful reform would be not just costless, but cost-saving. Effectiveness: very high in particular cases. Cost: negative. Politics: difficult to very difficult. People with a vested interest in existing policies fight hard to retain them. Environmental agencies don’t tend to fight for this, but there could be great benefits if they did.

In my judgement, for Australia, the top priorities should be strategies 2 and 5 followed by 6. Strategy 4 has good potential in certain cases. If these four strategies were delivered, the case for strategy 1 would be greatly increased (once the politics made that feasible). To succeed, strategies 2, 5 and 6 would need an investment in training and expert support within environmental organisations. Over time, in those environmental organisations that don’t already perform well in relation to strategies 2, 5 and 6 (i.e. most of them), there may be a need for cultural change, which requires leadership and patience.

In Europe and the USA, my first choice would be strategy 7, if it was politically feasible. After that, 2, 5, 6 and 4 again.

Further Reading

Garrick, D., McCann, L., Pannell, D.J. (2013). Transaction costs and environmental policy: Taking stock, looking forward, Ecological Economics 88, 182-184. Journal web site

Pannell, D.J., Roberts, A.M., Park, G. and Alexander, J. (2013). Improving environmental decisions: a transaction-costs story, Ecological Economics 88, 244-252. Journal web siteIDEAS page

Pannell, D.J. (2009). Technology change as a policy response to promote changes in land management for environmental benefits, Agricultural Economics 40(1), 95-102. Journal web page ◊ Prepublication version

Pannell, D.J. (2013). Ranking environmental projects, Working Paper 1312, School of Agricultural and Resource Economics, University of Western Australia. IDEAS page ◊ Blog series

230 – Future climate change and wheat yields in Western Australia

The wheat-growing areas of Western Australia are predicted to experience adverse climate change during the 21st century. Of the three components of change (rainfall, temperature and CO2) the first two are somewhat uncertain, but current modelling evidence suggests that great pessimism about future yields is probably not warranted. 

Although there was quite substantial climate change in the Western Australia wheatbelt during the 20th century, it had little or no adverse consequences for wheat yields (PD229). Of course, it doesn’t follow that the same will hold during the 21st century. That will depend on the details of how much change occurs, and at what time of year it occurs. These details are highly uncertain.

Even the general pattern of future climate change is inherently uncertain, particularly in the long term. It depends in part on global emissions of greenhouse gases, which in turn depend on economic activity, technology change and climate policy measures over the relevant time frame. Uncertainty about each of these factors is high and increases with the length of time frame. In addition, the world’s climatic system is complex, chaotic and imperfectly understood, so that there is additional uncertainty inherent in the results of global circulation models, the tools used to predict climate.

Uncertainty is greater at the regional scale than at the global scale. And it is greater still when it comes to the within-year timing of changes in rainfall and temperature. We really have little idea about those timings, although they can be crucial in determining the consequences for crop yields (PD229).


Adverse changes in rainfall probably have the largest potential to reduce crop yields. Large reductions in September rainfall would be especially damaging. Unfortunately, rainfall is the factor about which we have the greatest uncertainty. Loosely speaking, regions that are already relatively wet are predicted to get wetter, and regions that are relatively dry (such as the Western Australian wheatbelt) are predicted to get drier, but in truth the models aren’t good enough to give us confidence about what will really happen in any particular place. CSIRO (2007) (somewhat heroically) predicted changes in annual average rainfall for the south-west of -20% to +5% by 2030 and -60% to +10% by 2070 relative to the period 1980-1999.

If the real results do fall within these ranges, 2030 would probably not be catastrophic, unless it includes a large reduction in early-spring rainfall. Clearly, -60% in 2070 would be catastrophic, but it’s the extreme case. Something nearer the midpoint of that range (-20%) would probably be damaging but not catastrophic. The real message about 2070 is that we have huge uncertainty about what rainfall will do – the range of the CSIRO predictions is 70%!


By 2050, increases in temperature could perhaps be somewhere in the range of 1 to 3 °C. A positive impact on yield of temperature increases up to 1–3 °C has been reported from relevant crop modelling results when assuming that temperatures increase by the same amount every day across the growing season. Positive yield impacts come from accelerated plant development, leading to avoidance of high maximum temperatures and water stress during grain filling.

On the other hand, if the higher temperatures happen to include an increased frequency of extreme temperatures during grain filling, the result could be very negative. There is a potential for yield reductions of 5% for each day of extreme heat during grain filling.

Again, there is high uncertainty. Future temperature changes could be positive, negative or neutral for crop yields in Western Autralia.


CO2 fertilization of crops is a significant part of the climate/CO2 story (Attavanich and McCarl, 2012). In future, CO2 concentrations are likely to increase at about about 4 ppm per year. This is predicted to increase yields of current wheat cultivars in Western Australia by 15–30% over the next 50 years (Asseng and Pannell, 2012). Percentage increases in yield are likely to be greater in dryer agricultural regions, mainly due to increased water-use efficiency. Compared to the changes in rainfall  and temperature, the increases in CO2 are fairly certain. They are also constant all year, avoiding the tricky problem of predicting the within-year distribution of changes.

Highly adverse changes in rainfall and temperature would be really catastrophic for farmers in the Western Australian wheatbelt, but more likely the overall impacts will be moderate, particularly when the positive yield effects of CO2 fertilization are factored in.

Reinforcing that judgement, another new paper (Potgieter et al., 2012) concludes that, under a high-emissions scenario, different shires would see changes in crop yields by 2050 of −5% to +6% across most of Western Australia (and Victoria and southern New South Wales), even without allowing for CO2 fertilization.

I was surprised at how favourable those results are, especially considering that they are for a high-emission scenario. If they are accurate, then any negative impacts would be outweighed by the positive effects of CO2 fertilization.

I remember reading the original report of the Garnaut review, and being struck by how much it emphasised the risks to agriculture when justifying Australia’s need for a strong policy response to climate change. There certainly are risks to agriculture, but this evidence suggests that they are not as compelling a policy driver as Garnaut indicated. If Potgieter et al. are right, then I would expect that the ongoing decline in public investment in agricultural research will have far worse consequences for agriculture.

This Pannell Discussion draws on part of a paper I’ve recently published with Senthold Asseng, who’s now at the University of Florida (Asseng and Pannell, 2012).

Further reading

Asseng, S. and Pannell, D.J. (2012). Adaptating dryland agriculture to climate change: farming implications and research and development needs in Western Australia, Climatic Change (forthcoming).

Attavanich, W. and McCarl, B. (2012). The effect of climate change, CO2 fertilization, and crop production technology on crop yields and its economic implications on market outcomes and welfare distribution, Agricultural and Applied Economics Association, 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania, IDEAS page for this paper.

Potgieter, A., Meinke, H., Doherty, A., Sadras, V.O., Hammer, G., Crimp, S. and Rodriguez, D. (2012). Spatial impact of projected changes in rainfall and temperature on wheat yields in Australia, Climatic Change (forthcoming).

229 – Past climate change and wheat yields in Western Australia

The wheat-growing areas of Western Australia experienced substantial climate change (particularly rainfall decline) during the 20th century. However, the resulting impacts on wheat yields were negligible, even after factoring out changes in technology and prices.

Western Australia is by far the largest grain-crop-producing state of Australia, and wheat is by far its main crop.

The wheatbelt underwent significant climate change during the 20th century, commencing even before climate change was a high-profile issue. The region has had a 20% rainfall decline over the past 110 years, more than any other wheat-growing region in Australia.

There has been a specific pattern to the rainfall decline, with most of it occurring in winter. Figure 1 shows a typical example, for the Mullewa region.

Figure 1. Average monthly rainfall for Mullewa for 1945–1974 (filled bars) and 1975–2008 (open bars)


The other important climatic variable is temperature. Average temperature in the region has increased slightly (0.8 °C) over the past 50 years, but there has been a disproportionate increase in the frequency of hot days during grain filling (Asseng et al. 2011), when wheat yields are adversely affected by high temperatures. Some of that impact may have been off-set by increases in temperature during winter, which helps to increase yields.

Ludwig et al. (2009) used crop modelling to investigate the effects of past climate change on crop yields over the past 60 years, across various locations of the Western Australian wheatbelt. Remarkably, they concluded that there has been no change in wheat yield potential. The reasons they proposed for the lack of impact of reduced rainfall are:

  1. Crops have low demand for water during the cool winter months in which the decline has occurred
  2. Given the unpredictability of weather, farmers do not apply sufficient inputs (particularly fertilizer) to achieve the higher yields that are theoretically possible in wet years, and
  3. Most Western Australian soils have low water-holding capacity, so a large proportion of unused water is lost below the root zone of crops.

A third change, not considered by Ludwig et al. (2009), has been the increase in atmospheric CO2. Higher CO2 has presumably contributed to the climatic changes (especially tempterature) but has another effect on crop yields, through so-called “CO2 fertilization” (Attavanich and McCarl, 2012). Increases in atmospheric CO2 concentration over the past 50 years have increased wheat yields in Western Australia by approximately 2–8 %.

Overall, I think it’s quite interesting and surprising that, despite really significant changes in climate in the region, these changes have had no significant negative impact on yields, especially when CO2 fertilization is factored in.

It highlights that the specific details of the climate changes (such as their within-year timing) really matter. Changes that would be damaging at one time of the year can be benign at another. This makes it even harder to accurately predict the impacts of future climate changes, even if we get their average magnitudes right (which is already tough).

At the same time as climate change was having no impact on wheat yields in Western Australia, other things were having big positive impacts, including changes in crop varieties, increased fertiliser use, herbicides, reduced tillage, improved machinery allowing earlier sowing, retention of crop residues, and the use of ‘break’ crops that reduce root diseases. These have combined to increase average wheat yields in the region by around 100% over the past 30 years.

Some scientist have argued that farmers in this region should already be making changes to adapt to climate change. In the light of these results, that advice seems misguided.

This Pannell Discussion is based on part of a paper I’ve recently published with Senthold Asseng, who’s now at the University of Florida (Asseng and Pannell, 2012).

Further reading

Asseng, S. and Pannell, D.J. (2012). Adaptating dryland agriculture to climate change: farming implications and research and development needs in Western Australia, Climatic Change (forthcoming).

Attavanich, W. and McCarl, B. (2012). The effect of climate change, CO2 fertilization, and crop production technology on crop yields and its economic implications on market outcomes and welfare distribution, Agricultural and Applied Economics Association, 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania, IDEAS page for this paper.

Ludwig, F., Milroy, S.P., Asseng, S. (2009). Impacts of recent climate change on wheat production systems in Western Australia. Climatic Change 92: 495–517.

228 – Majority opinion

This week I saw a senior bureaucrat try to counter dissenting views on a government report by arguing that the great majority of people agreed with it. This is a highly flawed argument.

I was at a public seminar this week, at which the leader of a government inquiry was outlining the findings from the resulting report. He made the observation that the great majority of people seemed to agree with the findings, but that there was a small but vocal minority who did not.

Most of the audience seemed to be broadly on side with the speaker, bearing out his claim, but at least one took a very different position. During question time she made a vehement statement that amounted to a denunciation of the entire report.

The speaker responded in kind. He said he thought she probably hadn’t read the report. When she reacted angrily to that, he asserted, “then you didn’t understand it” and glared at her. Later he once again referred to “the small but vocal minority”, this time adding, “who should be ignored”.

It had been quite an aggressive comment, so perhaps the aggressive response was fair enough in a way, but the result was to close off debate. I guess that’s what the speaker wanted but I don’t think it was appropriate, and it didn’t go down well with some of the audience.

While the exchange of fire was exciting, the main thing I’m going to focus on is the speaker’s implication that the views of a group of people must be wrong and should be ignored because they are the views of a small minority. That’s a terrible argument. If evidence and logic is with the minority, it doesn’t matter how few in number they are. As Einstein said in response to a Nazi pamphlet titled 100 authors against Einstein, “If I had been wrong, one would have been enough”.

I’m not saying that the commenter was right. I haven’t read the report in question, so I can’t judge. All I’m saying is that the speaker was wrong to point to the number of people who agreed with the report as an indication that it was sound.

I have personal experience of being in an absolutely miniscule minority on a controversial issue, but eventually being accepted as the one who was correct. The issue was dryland salinity in Australia. My analysis in the early 2000s, drawing together the hydrogeology, economics and sociology of salinity, led me to conclude that the existing policy emphasis on integrated catchment management was misguided in most cases (Pannell, 2001a, 2001b).

Also, salinity management recommendations at the time emphasised the need for all farmers in a catchment to cooperate due to their supposed hydrological inter-dependence. I concluded that this too was misguided, initially for Western Australia (Pannell et al., 2001) and later more generally (Beverley et al., 2012).

For a long time I was the only one who was publicly putting these view, which ran counter to the way that almost everybody was thinking about the problem. An implication of my conclusions was that many millions of dollars were being wasted in public programs to fight salinity.

When I tried to put my position in conferences and meetings, I often generated strong negative reactions, including derision and anger. Many people working in the area said that I didn’t know what I was talking about and rejected my arguments out of hand. One notable incident involved a very public tirade of abuse from a fellow economist following my Presidential Address to the Australian Agricultural and Resource Economics Society in 2001.

Over time, the weight of evidence supporting my position got stronger and stronger (e.g. Barrett-Lennard et al., 2005), and I won people over, or maybe just wore them down. By the time of the Second International Salinity Forum in Adelaide in 2008, my views that had seemed heretical to many in 2001 had become the new orthodoxy.

The point is, it doesn’t matter how small the minority is, they might be right. Logic and evidence is what matters, not weight of numbers.

In fact, whenever there is a really important advance in knowledge that overturns a previous misconception, by definition, the person with the new insight is initially in the small minority.

The salinity experience has made me particularly attuned to the possibility that the majority can be wrong. As a result, I worry about the heavy reliance on scientific consensus as an argument in the climate debate. The consensus might well be right, but the fact that there is a consensus is not, in itself, an argument that should be convincing. There was an almost unanimous consensus about these two aspects of salinity management and policy that turned out to be wrong.

Further reading

Barrett-Lennard, E.G., George, R.J., Hamilton, G., Norman, H.C., Masters, D.G. (2005). Multi-disciplinary approaches suggest profitable and sustainable farming systems for valley floors at risk of salinity, Australian Journal of Experimental Agriculture 45: 1415–1424. Journal web site here

Beverly, C., Roberts, A., Hocking, M., Pannell, D. and Dyson, P. (2011). Using linked surface-groundwater catchment modelling to assess protection options for environmental assets threatened by dryland salinity in southern-eastern Australia, Journal of Hydrology 410: 13-30. Journal web site here

Pannell, D.J. (2001a). Salinity policy: A tale of fallacies, misconceptions and hidden assumptions, Agricultural Science 14(1): 35-37. Here

Pannell, D.J. (2001b). Dryland Salinity: Economic, Scientific, Social and Policy Dimensions, Australian Journal of Agricultural and Resource Economics 45(4): 517-546. Journal web site here ♦ IDEAS page for this paper

Pannell, D.J., McFarlane, D.J. and Ferdowsian, R. (2001). Rethinking the externality issue for dryland salinity in Western Australia, Australian Journal of Agricultural and Resource Economics 45(3): 459-475. Journal web site here ♦ IDEAS page for this paper

Pannell, D.J. and Roberts, A.M. (2010). The National Action Plan for Salinity and Water Quality: A retrospective assessment, Australian Journal of Agricultural and Resource Economics54(4): 437-456. Journal web site here ♦ IDEAS page for this paper

226 – Modelling versus science

Mick Keogh, from the Australian Farm Institute, recently argued that “much greater caution is required when considering policy responses for issues where the main science available is based on modelled outcomes”. I broadly agree with that conclusion, although there were some points in the article that didn’t gel with me. 

In a recent feature article in Farm Institute Insights, the Institute’s Executive Director Mick Keogh identified increasing reliance on modelling as a problem in policy, particularly policy related to the environment and natural resources. He observed that “there is an increasing reliance on modelling, rather than actual science”. He discussed modelling by the National Land and Water Resources Audit (NLWRA) to predict salinity risk, modelling to establish benchmark river condition for the Murray-Darling Rivers, and modelling to predict future climate. He expressed concern that the modelling was based on inadequate data (salinity, river condition) or used poor methods (salinity) and that the modelling results are “unverifiable” and “not able to be scrutinised” (all three). He claimed that the reliance on modelling rather than “actual science” was contributing to poor policy outcomes.

While I’m fully on Mick’s side regarding the need for policy to be based on the best evidence, I do have some problems with some of his arguments in this article.

Firstly, there is the premise that “science and modelling are not the same”. The reality is nowhere near as black-and-white as that. Modelling of various types is ubiquitous throughout science, including in what might be considered the hard sciences. Every time a scientist conducts a statistical test using hard data, she or he is applying a numerical model. In a sense, all scientific conclusions are based on models.

I think what Mick really has in mind is a particular type of model: a synthesis or integrated model that pulls together data and relationships from a variety of sources (often of varying levels of quality) to make inferences or draw conclusions that cannot be tested by observation, usually because the issue is too complex. This is the type of model I’m often involved in building.

I agree that these models do require particular care, both by the modeller and by decision makers who wish to use results. In my view, integrated modellers are often too confident about the results of a model that they have worked hard to construct. If such models are actually to be used for decision making, it is crucial for integrated modellers to test the robustness of their conclusions (e.g. Pannell, 1997), and to communicate clearly the realistic level of confidence that decision makers can have in the results. In my view, modellers often don’t do this well enough.

But even in cases where they do, policy makers and policy advisors often tend to look for the simple message in model results, and to treat that message as if it was pretty much a fact. The salinity work that Mick criticises is a great example of this. While I agree with Mick that aspects of that work were seriously flawed, the way it was interpreted by policy makers was not consistent with caveats provided by the modellers. In particular, the report was widely interpreted as predicting that there would be 17 million hectares of salinity, whereas it actually said that there would be 17 million hectares with high “risk” or “hazard” of going saline. Of that area, only a proportion was ever expected to actually go saline. That proportion was never stated, but the researchers knew that the final result would be much less than 17 million. They probably should have been clearer and more explicit about that, but it wasn’t a secret.

The next concern expressed in the article was that models “are often not able to be scrutinised to the same extent as ‘normal’ science”. It’s not clear to me exactly what this means. Perhaps it means that the models are not available for others to scrutinise. To the extent that that’s true (and it is true sometimes), I agree that this is a serious problem. I’ve built and used enough models to know how easy it is for them to contain serious undetected bugs. For that reason, I think that when a model is used (or is expected to be used) in policy, the model should be freely available for others to check. It should be a requirement that all model code and data used in policy is made publicly available. If the modeller is not prepared to make it public, the results should not be used. Without this, we can’t have confidence that the information being used to drive decisions is reliable.

Once the model is made available, if the issue is important enough, somebody will check it, and any flaws can be discovered. Or if the time frame for decision making is too tight for that, government may need to commission its own checking process.

This requirement would cause  concerns to some scientists. In climate science, for example, some scientists have actively fought  requests for data and code. (Personally, I think the same requirement should be enforced for peer-reviewed publications, not just for work that contributes to policy. Some leading economics journals do this, but not many in other disciplines.)

Perhaps, instead, Mick intends to say that even if you can get your hands on a model, it is too hard to check. If that is what he means, I disagree. I don’t think checking models generally is harder than checking other types of research. In some ways it is easier, because you should be able to replicate the results exactly.

Then there is the claim that poor modelling is causing poor policy. Of course, that can happen, and probably has happened. But I wouldn’t overstate how great a problem this is at the moment, because model results are only one factor influencing policy decisions, and they often have a relatively minor influence.

Again, the salinity example is illustrative. Mick says that the faulty predictions of salinity extent were “used to allocate funding under the NAP”. Apparently they influenced decisions about which regions would qualify for funding from the salinity program. However, in my judgement, they had no bearing on how much funding each of the 22 eligible regions actually received. That depended mainly on how much and how quickly each state was prepared to allocate new money to match the available Federal money, coupled with a desire to make sure that no region or state missed out on an “equitable” share (irrespective of their salinity threat).

The NLWRA also reported that dryland salinity is often a highly intractable problem. Modelling indicated that, in most locations, a very large proportion of the landscape area would need to be planted to perennials to get salinity under control. This was actually even more important information than the predicted extent of salinity because it ran counter to the entire philosophy of the NAP, of spreading the available money thinly across numerous small projects. But this information, from the same report, was completely ignored by policy makers. The main cause of the failure of the national salinity policy was not that it embraced dodgy modelling about the future extent of salinity, but that it ignored much more soundly based modelling that showed that the strategy of the policy was fundamentally flawed.

Mick proposes that “Modellers may not necessarily be purely objective, and “rent seeking” can be just as prevalent in the science community as it is in the wider community.” The first part of that sentence definitely is true. The last part definitely is not. Yes, there are rent-seeking scientists, but most scientists are influenced to a greater-or-lesser extent by the explicit culture of honesty and commitment to knowledge that underpins science. The suggestion that, as a group, scientists are just as self-serving in their dealings with policy as other groups that lack this explicit culture is going too far.

Nevertheless, despite those points of disagreement, I do agree with Mick’s bottom line that “Governments need to adopt a more sceptical attitude to modelling ‘science’ in formulating future environmental policies”. This is not just about policy makers being alert to dodgy modellers. It’s also about policy makers using information wisely. The perceived need for a clear, simple answer for policy sometimes drives modellers to express results in a way that portrays a level of certainty that they do not deserve. Policy makers should be more accepting that the real world is messy and uncertain, and engage with modellers to help them grapple with that messiness and uncertainty.

Having said this, I’m not optimistic that it will actually happen. There are too many things stacked against it.

Perhaps one positive thing that could conceivably happen is adoption of Mick’s recommendation that “Governments should consider the establishment of truly independent review processes in such instances, and adopt iterative policy responses which can be adjusted as the science and associated models are improved.” You would want to choose carefully the cases when you commissioned a review, but there are cases when it would be a good idea.

Some scientists would probably argue that there is no need for this because their research has been through a process of “peer reviewed” before publication. However, I am of the view that peer review is not a sufficient level of scrutiny for research that is going to be used as the basis for large policy decisions. In most cases, peer review provides a very cursory level of scrutiny. For big policy decisions, it would be a good idea for key modelling results to be independently audited, replicated and evaluated.

Further reading

Keogh, M. (2012). Has modelling replaced science? Farm Institute Insights 9(3), 1-5.

Pannell, D.J. (1997). Sensitivity analysis of normative economic models: Theoretical framework and practical strategies. Agricultural Economics 16(2): 139-152. Full paper here ♦ IDEAS page for this paper

Pannell, D.J. and Roberts, A.M. (2010). The National Action Plan for Salinity and Water Quality: A retrospective assessment, Australian Journal of Agricultural and Resource Economics54(4): 437-456. Journal web site here ♦ IDEAS page for this paper