Budgeting and Planning Historical Context Mainstreaming and CPIERs - - PowerPoint PPT Presentation

Climate Change Budgeting and Planning

Historical Context

Rio 1992 Limits to Growth 1970s Climate Science and IPCC GCF $100bn Kyoto UNFCCC Mainstreaming and CPIERs since 2011

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Source: IPCC AR5

Terminology

• Adaptation: actions that reduce the vulnerability to CC
• Mitigation: actions that reduce net emissions
• Low regret: actions that are viable if CC doesn’t happen
• Climate Risky: actions that are not viable without CC
• Mainstreaming: integrated in budget, not separate project
• Performance Based Budgeting: budget linked to results
• Leveraging: added private finance created by public exp.

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Climate Public Expenditure and Institutional Reviews

Nepal CPEIR 2011 Policy, institutions, expenditure, PFM, SNA Bangladesh CPEIR 2012 Policy, institutions, expenditure, PFM, SNA Leading to tagging work Thailand CPEIR 2012 Policy, institutions, expenditure, PFM, SNA Leading to MOAC initiative Cambodia CPEIR 2010 CCFF 2014 Policy, institutions, expenditure, PFM, SNA Costed action plans to support CC Strategy Links expenditure to benefits Samoa CPEIR 2012 Policy, institutions, expenditure, PFM, SNA Indonesia MFF 2013 Mitigation spending, effectiveness, impact Leads to tagging, GPB Strategy Vietnam Ongoing Policy, institutions, expenditure, PFM, SNA Links expenditure to CC policies Africa Ongoing Expenditure and institutions Philippines Ongoing Latin America Planned

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What is Climate Finance?

• UNFCCC & HLAG (on the $100bn), based on source
• f finance
• CPI definition, based on tracking all steps
• Distinguishes between direct and indirect
• OECD DAC definition for donor tracking
• Principle objective (2), if the aim is direct and explicit
• Significant objective (1), if secondary aim declared
• CPEIR definitions from country perspective
• High-mid-low-marginal relevance
• Scores (0%-100%)

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CPEIR Definitions

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Comparison of Classifications

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Implications of Differences

Bottom Top Bangladesh Cambodia Renewable energy 75%, if motivated mainly by cost 100% (mitigation) 75%-100% 80% Electricity (non-RE)

• ve, if seen as increasing emissions

25% (?) if used for reducing losses or stopping use of fuelwood or generators Some 25% Some 0% Forestry 50%, if motivated mainly by incomes or biodiversity 100%, if motivated entirely by mitigation 75%-100% 80% Disaster management 25%, reflecting the increased frequency of extreme climate events 100%, if seen as fully relevant to climate change 80% Disaster relief 0%, if seen as related only to current extreme climate, not changes 100%, if seen as part of a deliberate adaptation strategy 80% Water supply and water quality 25%, reflecting increase in extreme climate events and/or rainfall/ET trends 100%, if seen as fully relevant to climate change,

• r if all used for climate proofing

Some 80% Some 50% Some 25% Irrigation 25% (?) if considering only the increased frequency 100%, if all for climate proofing Some 75%-100% 50% Agriculture Some 75%-100% Some 50%-75% Treated a livelihoods Biodiversity/conservation 0%, if unrelated to climate 50% (?) if partly affected by climate change 50% Eco-tourism 0% if not contributing to household resilience or climate-related biodiversity 50% (?) if giving incomes for climate vulnerable,

• r helping climate-related biodiversity

50% Livelihoods 0%, if not helping the climate vulnerable 50% (?) if highly focused on increasing the incomes for climate vulnerable 25%-50% Some 50% Some 25% Social protection (<) 25%, if designed primarily for current day risks 100%, if specifically designed to respond to increased climate risks 50%-75% 0% Railway (<) 25%, if impact on emission is small 50%, if impact on emissions is large 50% Roads and infrastructure (<) 25%, if some proofing undertaken, of if there are secondary benefits to welfare of climate vulnerable households 100%, if all spent on climate proofing Some 75%-100% Some 50%-75% Some 25%-50% Some 0%-25% Some 80% Some 50% Some 25% Some 0% Health (climate sensitive diseases) 25% (?) if considering only the increased frequency 100%, if all for climate proofing 80% Health (General) <10%, if no focus on climate diseases (<?) 25%, if climate diseases important 25% Planning for CC 100% 100% 25%-50% 80% Governance/planning (General) 0%, if unrelated to climate change (<?) 25%, if supporting systems that could help climate planning 25%

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Some of the Programme most difficult to classify

• Disaster relief – disasters will become more common,

but should all relief to existing climate count

• Climate proofing – knowing what part of the design is

for extreme events and is it protecting against existing or future extremes

• Livelihoods – all vulnerability studies show that

increased incomes/savings is the best way to reduce vulnerability, but that includes a huge part of public expenditure

• Social protection – it does reduce vulnerability, but it

is rarely linked with CC

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Key Elements of CC Planning and Budgeting

Actual Expenditure Effectiveness Damage from Climate Change CC Strategy Budget Influence Impact Private Sector and Revenue Policy

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Application of methodology

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Application of methodology 2

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CC Damage and Loss

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CC Modelling - IPCC

Best B1 = high growth esp. in tertiary sectors A1T = high growth, non-fossil energy A2 = slow growth and technological change B2 = mid growth, regional solutions A1B = high growth mixed technical change Worst A1FI = high growth and fossil intensive

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Source: IPCC AR4 Source: SREX Report 2012 Source: IPCC AR4

Downscaled models

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Supnithadnaporn et al

CC Damage and Loss

• Global – Stern Review suggested 5% of GDP ‘now and forever’
• Agriculture
• Cambodia – last 10 years loss to flood, drought and dry spell is 0.71%, so by 2050

losses will be 1.42%, under B1 with no adaptation – loss is each year, accumulated

• FAO suggest varies from 5% to 75% lower in 2050
• WB in India suggest degradation of farmland reduced national GDP by 1.7% in 2009
• Forestry losses depend on the national situation with forest stocks

and dependency of GDP on forest exploitation

• Energy
• Infrastructure losses 0.07% cumulative in Cambodia
• Pollution related to urban emissions has health costs that reduce urban labour

productivity by 4.2% in US, 3.1% in China, 3.0% in India, 1.1%-1.5% in Philippines

• Accelerated infrastructure degradation costing 0.71% of GDP by

2050 in Cambodia, based on engineers estimates

• Damage to people/property from storms/floods cost average of

0.12% of GDP in last 10 years in Cambodia and will double by 2050

• WHO suggests increased burden from climate sensitive diseases: 0.85%

GDP

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Thailand’s Second National Communication to the UNFCCC

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• Climate change projections
• Uncertainty over trends in precipitation, but likely to increase
• But climate variability and the frequency of extreme events is

already increasing and this will continue

• And sea level rise is also clear
• Policy response to climate change
• Disaster reduction management for floods, droughts etc
• Support for farmers response
• Some complex hydrology modelling
• Early research on health effects
• Support for local level resilience plans
• Mitigation is 70% power, but also 23% agriculture, mainly

from rice paddy and livestock

• Food security is essential, so agricultural mitigation is win-win

Other Research on CC Impact in Thailand and SE Asia

• TRF doing lots of work on climatology
• ADB 2009. PAGE2002 model. Total cost of CC in SE Asia is

6.7% GDP/year by 2100. Adaptation of 0.2% GDP/year will reduce costs by 1.9% of GDP.

• CSIRO 2010. Aquacrop model. Rice yields up 8%-28% by

2050 in Thailand/Laos. Mixed in rest of SE Asia.

• DAI USAID 2013 Mekong ARCC. Aquacrop model. Most

crop yields declining by 3% to 12% by 2050, but some parts of Thailand increase by 5%.

• WorldFish 2013 Coastal SE Asia, using changes in

vulnerability index.

• WB global EACC, incl. Vietnam. IMPACT model (CERES).

GDP growth down 0-3% by 2050. Agri VA down 6-14%.18

Selected International Analysis

• Lots of work with crop water modelling, based on

FAO I&D Papers 24, 33 & 56 (eg CERES, CLICROP, CROPWAT, Aquacrop, WOFOST, DSSAT …)

• Models integrating crop response with economics

(eg IFPRI IMPACT, FAO MOSAICC …)

• Some statistical regression analysis (eg US/EU

Ricardian studies and UK CCRA time series)

• Zoning produces more varied projections but

explores farmer response (eg in Brazil, FAO Ecocrop and FAO/IIASA Agro-Ecol Zone model)

• HRW review of value of sophisticated models

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Influence on Strategy and the Budget

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Budget Influence

• CC Planning and Budgeting ensures that existing

spending is used in the most efficient ways

• It facilitates marginal shifts towards those

programmes that can deliver higher benefits with CC (and away from those that are climate risky)

• It gives a good overview of uncertainty about CC

and a good mix of low and high regret options

• It guides any additional financing that may be

available, either international or domestic

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Benefits to the Budget of CC Planning and Budgeting in MOAC

• Smaller increases in yield variability reduces cost of

rice price support and other social support schemes

• More resilient agricultural growth protects revenue

from crop processing

• If rural communities have their own adaptation, relief

for floods and droughts costs less

• Improved agricultural yields reduces pressure on

deforestation and emissions

• More resilient crop production reduces export

variability and so vulnerability to oil shocks (because food and oil prices are linked)

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Source: IMF Commodity Prices

Strategy Preparation

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CC Finance Scenarios

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Measuring Climate Expenditure

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CPEIR Headlines on Climate Expenditure

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Trends in Climate Spending

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Breakdowns

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Action Plans

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Benefits Analysis and Cost Effectiveness

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Main processes

• Any indicator that is linked to rainfall variability can be

assumed to double by 2050, as a working rule of thumb

• This change can be expected to take place linearly

between now and 2050, as a first approximation

• There may be some places where more detailed CC

evidence is available, especially on seasonality, but these are likely to be limited

• Trends in average rainfall are generally difficult to model

in SE Asia, so it may be best to leave these at present

• In a few cases, performance will be affected by

temperature (eg related to water balance, health …)

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Main sources of evidence

• Simulation models (eg the crop models, but also livestock …)
• may appear to require less data, but are only as good as the calibration, which does

require data (often they are used with international parameters – eg standard FAO values)

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Source: DDPM 2007 Source: Saito et al 2006

• Statistical analysis, either

geographical or time series

• because of the big

structural difference over time and space, this may be best used to cross-check

• ther evidence
• Expert opinion (including that of

farmers)

• essential to check with this and

especially valuable for more complex farmer response options

Effectiveness and Benefits

Has to accommodate three types of CC expenditure:

• A. Rescaling of existing expenditure simply because it

gives higher benefits

• B. Modifying existing expenditure (eg ‘proofing’)
• C. New dedicated actions (which could be the

proofing element of modifying expenditure)

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High and Low Regret and Climate Risky

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Mitigation Effectiveness

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Source: McKinsey 2010 Source: DNPI 2010 Source: Wetzelaer et al 2007

Impact of CC on Benefits - Type A

• Actions already producing

reasonable BCRs

• Benefits increased by CC (eg

because the action provides protection against temp/floods/drought and this protection becomes more important)

• These actions should get a

marginally higher priority

• The CC weight is the %

increase in benefits

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Impact of CC on Benefits - Type A

Note Increase in average yield 10% Risk related benefits/yield related 50% NPV Y1 Y2 Y3 Y4 Y5+ Costs ($/ha) Reserch investment 1 81 85 Extension 81 85 Benefits ($/ha) Without CC Average annual improved crop margins 2 276 16.5 16.5 16.5 16.5 16.5 Risk related benefits 3 138 8.25 8.25 8.25 8.25 8.25 With CC Average annual improved crop margins 276 16.5 16.5 16.5 16.5 16.5 Risk related benefits 276 16.5 16.5 16.5 16.5 16.5 Net Benefits ($/ha) BCR Without CC 2.56 252

• 145

25 25 25 25 With CC 3.42 390

• 137

33 33 33 33 CC relevance measure 33% 1 According to http://ageconsearch.umn.edu/bitstream/126037/2/1-PS-Birthal.pdf, the global investment in 2 According to above, typical yield increases are 10% to 15% from new varieties 3 According to above source, risk aversion benefits are typically an additional 50% above aggregate benefits 37

Impact of CC on Benefits: Type B

• Actions already producing

reasonable BCRs

• CC generates losses (mainly

because rehabilitation costs increase with floods)

• Proofing involves some

costs, but reduces the losses

• CC weight is the % change in

benefits

• The estimate of losses

contributes to aggregate GDP losses

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Adaptation Effectiveness

• 744

• 742

• 534

• 532

• 982

• 874

New, Unproofed New, Proofed Rehab, Unproofed Rehab, Proofed

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Supplementing Research with Expert Opinion

Benefit estimates based on whatever scientific evidence is available, Thailand, SE Asia or international. But they can also be based on expert opinion, eg using the structure below

1.5-2.0 = the benefit, on its own, is strongly positive and easily enough to justify the cost (public and/or private) 1.2-1.7 = the benefit, on its own, is just enough to justify the cost 0.7-1.3 = the benefit is about equal to the cost – probably not enough to justify, but not a disastrous waste of resources 0.3-0.8 = important contribution of benefits, but not enough to justify cost – so a secondary objective 0.1-0.4 = minor benefits, worth noting and protecting, but not at the expense of more importance benefits

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Benefits

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Benefits

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Conclusions from Samoan CBA

• Reliability of cost benefit analysis is subject to uncertainty and

subjectivity of assumptions

• The main output is not a quantitative analysis, but a qualitative

assessment of strengths and weaknesses found through the definition

• f the methodology.
• Adaptation projects are harder to assess than mitigation ones because

economic benefits are different from financial benefits.

• Benefits under a climate change scenario are higher than a no CC

scenario by 3.6% to 15%. This figure can be used to advocate for climate change expenditure.

• Policy makers should start to include in their policies and in their CBAs

the impact of the climate change because it can bias completely the profitability of an investment.

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Using Benefits as a Relevance Scores

The score = (B – A) / B, where

A = the benefits that would be generated by the action, if there was no CC B = the benefits that would be generated with CC

Potentially used for estimating appropriate top- up funding for CC finance

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A B

Modalities and the Private Sector

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Modalities

• Direct public expenditure
• National Climate Fund, considered in Samoa and

Cambodia, but full of challenges

• Transfers and subsidies
• Financial instruments through banks (eg loan

guarantees, cheap loans …)

• Regulatory controls and promotional work (eg the

whole of the UK Carbon Plan)

• Transfers to local government, with/without targeting
• Public awareness and capacity building

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Instruments and Leverage Ratios

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Patterns of Expenditure (CPI)

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Impact

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Impact

• Extent to which existing finance delivers the mitigation target
• r reduces the damage loss from CC
• (But international rule of thumb (Stern) that you can only

expect to avoid two thirds of the damage from CC)

• Indonesia mitigation 15%
• Cambodia adaptation 27.5%
• Complicated timing issues about investment now followed by

impact later, so we can’t leave it all until 2050 – in fact, the benefits of a constant level of spending now roughly matches the need to offset increasingly high damage/loss

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• To reduce CC damage loss by 2/3 would require about $1000m,

in 2018, when the low growth scenario is only $275m

• So the CCAP will achieve only 27.5% of what could be achieved
• Because adaptation funding usually gives higher BCRs that standard

investment, it is worth switching resources to adaptation

Avoiding lower GDP growth

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Reducing the losses

• There is a Green GDP Gap associated with resource loss
• There will also be a large reduction in GDP growth from CC

damage and further resource degradation

• To protect 7% growth (in Green GDP), and reverse these losses,

total public GE expenditure needs to rise from IDR 20tr in 2013 (weighted) to IDR 46tr in 2020 (data to be finalised), assuming switch to pro-private sector instruments

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Overall Conclusions

• So far, country systems for mainstreaming climate finance

have focused on description of patterns to reveal trends and the big picture

• Plus a review of the policies and institutions
• Next phase is to start using this to influence the budget by

linking it more clearly to policy

• Two main (complementary) ways being explored
• Classifying expenditure according to policies - Vietnam
• Linking expenditure to any existing results-based prioritisation –

Cambodia and Indonesia

• Both are challenging and need judgement
• But the costs of failing to mainstream could stall growth

completely in the more vulnerable countries, by 2050

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The END

(Thank goodness) and Thank You

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