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Carbon sequestration in a changing climate

Figure 1. Influence statistics
Source: Authors’ estimates. Abbreviation: DFFITS: Difference in fit(s).

Table 3. Robust least squares regression estimates
Dependent variable: FCBI
Method: Robust least squares
Variables Coefficient Standard error z‑statistic Prob.
AGRINC −0.0029 0.0002 −9.8422 0.0000
DEFORATE 3.5900 74,885 4.7960 0.0000
FMCP −0.0004 0.0002 −1.9609 0.0499
TEMP −80,334 27,711 −28,989 0.0037
RAINFALL −354,464.6 202,600.2 −1.7495 0.0802
URBANPOP −4.2186 1.5262 −2.7640 0.0057
C 4.3300 61,875 6.9899 0.0000
Method: Robust statistics
R 2 0.5546 Adjusted R 2 0.5086
Rw 2 0.8317 Adjusted Rw 2 0.8317
Rn statistic 145.0428 Prob (Rn ) 0.0000
2
2
Source: Author’s estimate. Abbreviations: AGRINC: Agricultural income; C: Constant; DEFORATE: Deforestation; FCBI: Forest Carbon
Benefit Indicator; FMCP: Forest management and conservation policies; Prob: Probability; RAINFALL: Rainfall vulnerability;
TEMP: Extreme temperature; URBANPOP: Urban population.

The annual deforestation rate showed a positive that absorb more carbon. According to Robinson,
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coefficient, indicating that the FCBI increases with planting trees provides greater short-term advantages
deforestation. Afforestation programs that accompany than long-term costs to biodiversity and the ecosystem;
deforestation may temporarily sequester carbon, which hence, deforestation should not be condoned. The
explains this paradoxical result. Although cutting forest sustainability index displayed a negative
down mature forests may release carbon, afforestation coefficient, suggesting an inverse relationship with
or restoration effort scan introduce younger trees the FCBI. Sustainable forest management practices,

Volume 22 Issue 1 (2025) 59 doi: 10.36922/AJWEP025050027