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Climate change’s impact on commodity prices: a new challenge for monetary policy

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Abstract

Climate change is a global issue that is driving up commodity prices such as food, energy and fertiliser. In this light, this study examines the effects of climate change on international commodity prices (food, energy, and fertilisers) from 1961 to 2020. The study employs a Bayesian Vector Autoregression (BVAR) model identified with sign restrictions. First, this study finds that climate change causes an increase in commodity prices, with the effect being greatest for energy, followed by fertilisers, and least for food prices. Second, this study also finds that climate change may have amplified the short-run impact of fertiliser and energy prices on food prices. Third, the estimates suggest that climate change may have a negative impact on global real GDP. Overall, this study’s findings of global climate change’s positive effects on commodity prices and a negative impact on global real GDP, suggests that monetary authorities may face higher inflation-output trade-off. Hence, this study argues that climate change may be a new challenge to monetary policy strategy, particularly in countries that are net importers of staple foods and have a large share of food items in their consumer price index.

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Data availability

The data that supports the findings of this study are available in United Nations Food and Agriculture Organization (FAOSTAT), World Bank Commodity Price Statistics (The Pink Sheet), and United States Environmental Protection Agency (EPA) Repositories.

Notes

  1. These include measures like regulating carbon emissions through a carbon tax, emission trading system (ETS), subsidies, tax credits, guarantees, and provision of infrastructure or technology regulation.

  2. Rising temperatures, altered precipitation, decreased oxygen, rising sea levels, extreme events, flooding, drought, increased storms and cyclones, heat waves, acidification and air pollution, and so on.

  3. Among the many measures taken to combat climate change and decarbonise the economy are the following: a carbon tax; subsidies; emission trading system, guarantees; technology regulation; targets and infrastructure; tax credits for renewable energy investment; renewable electricity portfolio standards; and tax credits for residential solar systems, electric vehicles, and weatherisation of both homes and business buildings.

  4. Also, as a result of changes in rainfall patterns or longer growing seasons, some climes can experience positive supply shocks from physical risks.

  5. An increase in the carbon tax or price increases the cost of generating fossil fuels, causing their prices to rise. Because oil is used in the manufacturing and distribution of the vast majority of commodities, an increase in its price raises production costs. Profit margins are stretched when production costs grow, and firms seek to boost prices to maintain markups (Garganas 2006).

  6. The net inflation benefit would be the difference between the inflationary effects of unchecked climate change and mitigation measures.

  7. Climate change is defined in this study as a significant change in the average global average temperatures and precipitation over a long period of time. Four factors influenced the selection of this definition. First, key institutions (EPA, NASA, FAO, NOAA) often report key indicators of climate change, such as temperature and precipitation anomalies. Second, even the Paris Agreement target was set on temperature anomalies. Third, Hansen and Lebede (1987) demonstrate that temperature anomalies, rather than absolute temperatures, are the most appropriate measure of climate change. Fourth, Dell et al. (2012), Acevedo et al. (2018), Mukherjee and Ouattara (2021), Faccia et al. (2021), and Iliyasu et al. (2023) used temperature and precipitation anomalies to proxy climate change in their studies. Thus, using this definition, this study can measure climate change using temperature and precipitation anomalies.

  8. Evidence suggests that climate change raises energy prices (Golombek et al. 2012; Wade and Jennings 2015), and an increase in energy prices affects the cost of nitrogen fertilisers (urea & ammonium nitrate), which are produced primarily from natural gas (FAO 2022). Also, the direct impact of climate change on fertiliser prices may stem from damages to physical capital.

  9. Biofuel and fertiliser productions, for example, are emission-intensive and climate-sensitive; hence, climate change or the implementation of a carbon tax can push up energy, fertiliser, and food prices differently.

  10. The natural question is how much of the pass-through of energy and fertiliser prices to food prices may be attributed to climate change shock.

  11. This is known as conditional or shock-dependent pass-through and it is defined as the ratio of the total (cumulative) effect of shock j on price p over the total (cumulative) effect of the same shock on the inputs prices s (ECB 2020, p. 10). For consistency, we replace the exchange rate with input cost in the ECB’s definition. The conditional pass-through is computed using the formula: \(PT_{{n,s}}^{\tau }=\frac{{\sum\nolimits_{{j=0}}^{\tau } {\partial {\pi _{t+j}}/\partial \varepsilon _{t}^{s}} }}{{\sum\nolimits_{{j=0}}^{\tau } {\partial \Delta {e_{t+j}}/\partial \varepsilon _{t}^{s}} }}\), (see; An et al. 2020, p. 18). This metric measures the pass-through of input costs to food price when movements in energy and fertiliser prices are induced by climate change rather than exogenous movements in input price itself.

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Author information

Authors and Affiliations

  1. Department of Economics, Ahmadu Bello University, Business School, Zaria, Nigeria

    Jamilu Iliyasu & Aliyu Rafindadi Sanusi

Authors
  1. Jamilu Iliyasu
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  2. Aliyu Rafindadi Sanusi
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Contributions

Aliyu Rafindadi Sanusi conceptualised the approach, interpreted and discussed the empirical findings, reviewed the text in its entirety, and contributed to the writing of the introduction. Jamilu Iliyasu conducted the estimation and wrote the initial drafts of the introduction, literature review, identification of the VAR model, and the conclusion section.

Corresponding author

Correspondence to Jamilu Iliyasu.

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Appendices

Appendix 1. VAR Model Lag Selection Results

VAR Lag Order Selection Criteria

Endogenous variables: GTA_C GPA_INCH ENERGY FERTILIZERS RGDP FOOD

Exogenous variables: C

Date: 04/04/23 Time: 15:04

Sample: 1961 2020

Included observations: 57

Lag

LogL

LR

FPE

AIC

SC

HQ

0

-86.46569

NA

1.03e-06

3.244410

3.459468

3.327989

1

240.8738

574.2798

3.78e-11

-6.978028

-5.472621*

-6.392975*

2

280.2355

60.76900*

3.50e-11*

-7.095983*

-4.300229

-6.009458

3

302.7128

29.96975

6.29e-11

-6.621503

-2.535400

-5.033504

  1. The maximum lag was set at 3, given the long-term nature of the phenomenon under investigation. We reckon that it is reasonable to expect climate change event to have up to three years lag effect
  2. LR sequential modified LR test statistic (each test at 5% level)
  3. *indicates lag order selected by the criterion

Appendix 2. VAR Model Stability Results

Roots of Characteristic Polynomial

Endogenous variables: GTA_C GPA_INCH

ENERGY FERTILISERS RGDP FOOD

Exogenous variables: C

Lag specification: 1 2

Date: 04/04/23 Time: 15:03

Root

Modulus

0.986579

0.986579

0.776757–0.019825i

0.777010

0.776757 + 0.019825i

0.777010

0.660536–0.261053i

0.710251

0.660536 + 0.261053i

0.710251

0.600075

0.600075

0.199654–0.543936i

0.579421

0.199654 + 0.543936i

0.579421

-0.232399–0.509161i

0.559691

-0.232399 + 0.509161i

0.559691

-0.121174–0.231209i

0.261038

-0.121174 + 0.231209i

0.261038

  1. No root lies outside the unit circle
  2. VAR satisfies the stability condition
figure a

Appendix 3. VAR Residual Serial Correlation LM Tests Results

VAR Residual Serial Correlation LM Tests

Date: 04/04/23 Time: 15:04

Sample: 1961 2020

Included observations: 58

Null hypothesis: No serial correlation at lag h

Lag

LRE* stat

df

Prob.

Rao F-stat

df

Prob.

1

27.71792

36

0.8371

0.752525

(36, 152.1)

0.8402

2

37.25727

36

0.4110

1.041396

(36, 152.1)

0.4173

3

24.68876

36

0.9229

0.664154

(36, 152.1)

0.9246

Null hypothesis: No serial correlation at lags 1 to h

Lag

LRE* stat

df

Prob.

Rao F-stat

df

Prob.

1

27.71792

36

0.8371

0.752525

(36, 152.1)

0.8402

2

70.22028

72

0.5374

0.963025

(72, 158.1)

0.5641

3

111.8822

108

0.3797

1.015927

(108, 133.3)

0.4631

  1. *Edgeworth expansion corrected likelihood ratio statistic

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Iliyasu, J., Sanusi, A.R. Climate change’s impact on commodity prices: a new challenge for monetary policy. Port Econ J 23, 187–212 (2024). https://doi.org/10.1007/s10258-023-00237-2

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