IDEAS home Printed from https://ideas.repec.org/a/ibn/ibrjnl/v9y2016i11p215-221.html

Climate Change as an Emerging Component of Project Risk in the Agriculture Sector: An Empirical Assessment

Author

Listed:
  • Kwame Adu-Gyamfi
  • Emmanuel Opoku

Abstract

Conditions of climate change are increasingly affecting projects, especially Agriculture projects, across the world. In this situation, climate change could pose a major risk factor in sectors such as the Agriculture sector. This paper empirically examines climate change indicators as a correlated factor of traditional risk factors. A self-reported questionnaire was used to collect data from 265 farmers affiliated to manufacturing organizations in Accra. Factor Analysis (Principal Components) and Pearson’s correlation test were used to present findings. We found that all indicators of the traditional and climate change factor produced a communality value of not less than 0.50. Moreover the climatic factor significantly correlates with the traditional factors at 5% significance level. It is therefore concluded that climate change is an emerging component of project risks.

Suggested Citation

  • Kwame Adu-Gyamfi & Emmanuel Opoku, 2016. "Climate Change as an Emerging Component of Project Risk in the Agriculture Sector: An Empirical Assessment," International Business Research, Canadian Center of Science and Education, vol. 9(11), pages 215-221, November.
  • Handle: RePEc:ibn:ibrjnl:v:9:y:2016:i:11:p:215-221
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ibr/article/view/62427/34296
    Download Restriction: no

    File URL: http://www.ccsenet.org/journal/index.php/ibr/article/view/62427
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Zihan & Daeipour, Mohamad & Xu, Hongyi, 2023. "Quantification and propagation of Aleatoric uncertainties in topological structures," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Pablo Pereira Álvarez & Pierre Kerfriden & David Ryckelynck & Vincent Robin, 2021. "Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning," Mathematics, MDPI, vol. 9(18), pages 1-25, September.
    3. Fokoué, Ernest, 2005. "Mixtures of factor analyzers: an extension with covariates," Journal of Multivariate Analysis, Elsevier, vol. 95(2), pages 370-384, August.
    4. van der Linde, Angelika, 2008. "Variational Bayesian functional PCA," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 517-533, December.
    5. Anahita Nodehi & Mousa Golalizadeh & Mehdi Maadooliat & Claudio Agostinelli, 2025. "Torus Probabilistic Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 42(2), pages 435-456, July.
    6. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    7. Dorota Toczydlowska & Gareth W. Peters & Man Chung Fung & Pavel V. Shevchenko, 2017. "Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principal Components," Risks, MDPI, vol. 5(3), pages 1-77, July.
    8. Hugo Queiroz Abonizio & Janaina Ignacio de Morais & Gabriel Marques Tavares & Sylvio Barbon Junior, 2020. "Language-Independent Fake News Detection: English, Portuguese, and Spanish Mutual Features," Future Internet, MDPI, vol. 12(5), pages 1-18, May.
    9. James Ming Chen & Mira Zovko & Nika Šimurina & Vatroslav Zovko, 2021. "Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM 2.5 Pollution," IJERPH, MDPI, vol. 18(16), pages 1-59, August.
    10. Bork, Lasse & Kaltwasser, Pablo Rovira & Sercu, Piet, 2022. "Aggregation bias in tests of the commodity currency hypothesis," Journal of Banking & Finance, Elsevier, vol. 135(C).
    11. Matteo Barigozzi & Marc Hallin, 2023. "Dynamic Factor Models: a Genealogy," Papers 2310.17278, arXiv.org, revised Jan 2024.
    12. Chen, Tao & Martin, Elaine & Montague, Gary, 2009. "Robust probabilistic PCA with missing data and contribution analysis for outlier detection," Computational Statistics & Data Analysis, Elsevier, vol. 53(10), pages 3706-3716, August.
    13. Pelin Okutan & Emre N. Otay, 2025. "Climate Change Risk Perception, Adaptive Capacity and Psychological Distance in Urban Vulnerability: A District-Level Case Study in Istanbul, Türkiye," Sustainability, MDPI, vol. 17(12), pages 1-29, June.
    14. Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    15. Wang, Shao-Hsuan & Huang, Su-Yun, 2022. "Perturbation theory for cross data matrix-based PCA," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    16. Cook, R. Dennis, 2022. "A slice of multivariate dimension reduction," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    17. Li, Min & Wang, Ruo-Qian & Jia, Gaofeng, 2020. "Efficient dimension reduction and surrogate-based sensitivity analysis for expensive models with high-dimensional outputs," Reliability Engineering and System Safety, Elsevier, vol. 195(C).
    18. Michael R. Keenan & Gustavo F. Trindade & Alexander Pirkl & Clare L. Newell & Yuhong Jin & Konstantin Aizikov & Andreas Dannhorn & Junting Zhang & Lidija Matjačić & Henrik Arlinghaus & Anya Eyres & Ra, 2025. "Orbitrap noise structure and method for noise unbiased multivariate analysis," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    19. Guo, Yiping & Li, Johnny Siu-Hang, 2025. "Robust parameter estimation for the Lee-Carter family: A probabilistic principal component approach," Insurance: Mathematics and Economics, Elsevier, vol. 125(C).
    20. Wentao Qu & Xianchao Xiu & Huangyue Chen & Lingchen Kong, 2023. "A Survey on High-Dimensional Subspace Clustering," Mathematics, MDPI, vol. 11(2), pages 1-39, January.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ibrjnl:v:9:y:2016:i:11:p:215-221. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.