Category Archives: Latest

235 – Ranking environmental projects 1

Environmental organisations need to rank projects that they could potentially invest in. Often it is done poorly. This post starts a series on how to do it better.

The funding available for environmental projects and policies is a small percentage of the money we would need to deal comprehensively with all environmental problems. As a result, whether we like it or not, we have to choose what we do and don’t protect. Even programs that don’t explicitly prioritise their environmental investments do so implicitly – they just do it in a non-transparent, and usually very poor, way.

In my experience, the difference in potential environmental outcomes between poor prioritisation processes and good ones is enormous.

Doing a good job of ranking the investment options is not that hard if you are aware of a few principles, but it seems to me that most people who are responsible for deciding how environmental funds get allocated are not aware of these principles. Indeed, some of the most commonly used approaches to ranking environmental projects are guaranteed to result in very poor rankings. As a result, we miss easy opportunities to deliver much greater environmental outcomes.

My aim in this series of posts is to outline a set of relevant principles and insights that will help environmental decision makers choose the best projects. My focus is on collecting and analysing the information needed to provide high-quality project rankings. There is another set of issues about how the rules of the program are designed to provide incentives for its participants to behave appropriately (e.g. Pannell and Roberts 2010), but I won’t be covering those here. I’ll be talking about information, calculations and clear thinking – stuff that is easy to get right if you know what you are doing.

My aim is to help with practical decision making. As a result, I’ll be talking about the possibility of cutting corners by simplifying aspects of the process. You’ll see that I’m not averse to well-considered simplifications, but very wary of the risk that some simplifications will sabotage the whole process. For a practical system, simplifications are essential, but bad simplifications are disastrous.

Throughout, I will be assuming that the aim is to provide the most valuable environmental outcomes for the available resources.

What is being ranked?

The first requirement is to be clear about what is being ranked. Sometimes programs set out to rank a set of projects that they might invest in. The projects should define what would be done, to which environmental assets, where, and by whom.

At other times, programs seek to rank a set of environmental assets, with no explicit project activities defined. (I’ll use the term “environmental asset” to refer to any identifiable feature, entity, place, or species that might become a target for investment.) There is a risk here – if you don’t define the project activities for an environmental asset, you cannot rank them on the basis of providing the most valuable outcomes.

The problem is that the environmental value for money depends on the answers to questions like, “what is the technical feasibility of protecting the asset?”, “to what extent would the community cooperate?” and “what would it cost to protect the asset?” However, those questions can only be answered for a particular set of actions or interventions.

To further illustrate the point, various different projects could be defined for the same environmental asset. One potential project might have very ambitious goals, aiming to return the asset to pristine condition, while another might aim for a moderate improvement in its condition. Some of these different projects for the same asset may offer relatively good value for money while others don’t (e.g. Roberts et al. 2012). So you cannot conclude that investing in any particular asset is good or bad without being clear about the project actions that will be undertaken.

If the analysis is limited to environmental assets, not projects, then it is important to be aware of what can and cannot be done with the results. What you can reasonably do is filter the assets to identify ones where it is relatively likely that a well-designed project would deliver worthwhile benefits. This could be done using variables such as:

  • the value or significance of the assets,
  • the levels of degradation they have already suffered or are likely to suffer in future, and
  • the feasibility of managing them (in a loose general sense that doesn’t require specification of particular management actions).

You should not be making final decisions about which assets received funding, because that does require the specification of projects. Rather, you would be concluding that some assets are probably not worth considering further, and so not worth developing projects for.

Even this is not without risks. Because you are not looking at all of the relevant information, there is a chance of excluding some assets that would actually be worth investing in. For example, you might exclude investment in a particular asset because it seems likely to provide only modest benefits, but if the cost of the project is low enough, it could still be worth doing. With this process of filtering assets, you would miss out on cases like that.

However, it still might be worth filtering assets as part of a more comprehensive process. Indeed that is exactly what we do in Step 1 of INFFER (the Investment Framework for Environmental Resources) (Pannell et al. 2012). This is a simplification we use to reduce the cost of the system. If we can knock out some potential investments based on partial information, it takes less work to properly evaluate and rank a reduced set of potential projects.

If you must make final investment decisions based on assets, not projects, you need to imagine a notional project for each asset. Even a rough-and-ready notional project definition would be better than nothing.

Further reading

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

Pannell, D.J., Roberts, A.M., Park, G., Alexander, J., Curatolo, A. and Marsh, S. (2012). Integrated assessment of public investment in land-use change to protect environmental assets in Australia, Land Use Policy 29(2): 377-387. Journal web site here ♦ IDEAS page for this paper

Roberts, A.M. Pannell, D.J. Doole, G. and Vigiak, O. (2012). Agricultural land management strategies to reduce phosphorus loads in the Gippsland Lakes, Australia, Agricultural Systems 106(1), 11-22. Journal web site here ♦ IDEAS page for this paper

 

 

 

233 – Journal refereeing

Peer review of research is a key mechanism for quality control used in science. Unfortunately, some reviewers (or referees) perform their task in a hard and heartless way.

Back in 2002 I published a poem about this in a refereed journal article. I’m pretty pleased with this – you don’t see many poems in refereed journals. This week, somebody told me that my poem had been included (with praise such as, “a beautiful piece of work”) on a web page of econometric poetry. I then did a search and, apart from finding the original paper, I found it reproduced on three other pages (here, here, here), and referred to on several more. Isn’t the web marvelous?

In case you haven’t seen it, here it is.

I’m The Referee
David J. Pannell

You’ve posted in your paper
To a journal of repute
And you’re hoping that the referees
Won’t send you down the chute

You’d better not build up a sense of
False security
I’ve just received your manuscript and
I’m the referee

This power’s a revelation
I’m so glad it’s come to me
I can be a total bastard with
Complete impunity

I used to be a psychopath
But never more will be
I can deal with my frustrations now that
I’m a referee

 

The poem is therapeutic, as was the paper it was published in (Pannell, 2002), so if you’ve suffered at the hands of referees, you might want to read that too.

Further reading

Pannell, D.J. (2002). Prose, psychopaths and persistence: personal perspectives on publishing. Canadian Journal of Agricultural Economics 50(2), 101–116. Here ♦ IDEAS page for this paper

231 – Selecting environmental projects: prioritizing really matters

Almost all environmental programs are short of resources. Picking the best projects can make a huge difference to the benefits generated by a program.

There are more environmental projects available than can be afforded – often many more.

For example, a large national program in Australia recently funded around 5% of proposals received. Of course, proponents have already selected from among the available projects to some degree, so it was probably not more than 1% of potential projects that actually got funded.

Within the set of potential environmental projects, heterogeneity is enormous. They vary widely on every parameter: scale, feasibility, riskiness, importance to the community, likely compliance, costs, time frame, and so on.

As a result, the best projects are much, much better than the rest, meaning that programs can perform much better if they choose their projects well. Unfortunately, the importance of this is under-appreciated by most people involved in managing or participating in environmental programs. Many funded projects are mediocre, while great projects go unfunded or underfunded.

To illustrate how much it matters to focus resources on the best projects, the figure below shows a graph of the benefits and costs of around 7000 potential environmental projects in Australia (from Fuller et al. 2010, courtesy of Richard Fuller). Benefits are measured as the project’s contribution to protecting heavily cleared vegetation types. (See Fuller et al. 2010 for details.) It’s an incomplete measure of benefits, but it’s still useful to illustrate my point.

Note that the benefit and cost axes have been expressed in log scale to allow very small projects to be distinguished on a graph that includes some very large projects. The ranges of benefits and costs are enormous.

If we had enough money to fund 5% of the projects, the set that would generate the largest environmental projects would be the 5% with the highest Benefit: Cost Ratios (BCRs). These are shown as green triangles in the graph.

For this data set, the average BCR of the best 5% is 330 times greater than for the median project. For the best 10%, the ratio is 200 times.

Clearly, if a program faced with these 7000 potential projects fails to fund the best ones, the loss to the environment would be extremely large. From an environmental perspective, it would be well worth putting resources, time and effort into evaluating the projects to identify the best ones.

The quality of information and analysis required to do this successfully is significant. It’s certainly do-able (PD#185), and it’s well worth doing, but it’s rarely done. It requires the sort of analysis that underpins a good conservation tender. I don’t believe that the tender process itself is the key factor – it’s the information and analysis.

Of course, picking cases with large potential BCRs is not enough. Delivering the potential benefits also requires that:

  • the project receives sufficient funding (see PD#210),
  • the project is well designed with a good internal logic,
  • the project uses appropriate delivery mechanisms,
  • the project is well implemented, and
  • the project is managed in an adaptive way to allow for learning.

But accurately picking the best projects is a great start.

Further reading

Boesso, G. And Kumar, K. (2007). Who or what really counts in a firm’s stakeholder environment: an investigation of stakeholder prioritization and reporting, “Marco Fanno” Working Paper N.51, IDEAS page for this paper

Fuller, R.A., McDonald-Madden, E., Wilson, K.A., Carwardine, J., Grantham, H.S., Watson, J.E.M., Klein, C.J., Green, D.C. & Possingham, H.P. 2010. Replacing underperforming protected areas achieves better conservation outcomes. Nature, 466, 365-367.

Pannell, D.J. (2011). Problems with environmental project prioritisation, Pannell Discussions 185. Here

Pannell, D.J. (2012). Under-estimating the costs of environmental protection, Pannell Discussions 210. Here

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).

Rainfall

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%!

Temperature

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

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). http://link.springer.com/article/10.1007/s10584-012-0623-1

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). http://link.springer.com/article/10.1007/s10584-012-0543-0

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). http://link.springer.com/article/10.1007/s10584-012-0623-1

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.