S. Hu, H. Qi, Z. Wang et al.
Environmental Science and Ecotechnology 30 (2026) 100682
fiber stacking area, recovered fiber stacking area, other stacking areas, wastewater treatment area, thermal power plant area and other built-up areas of the i-th type of PPP, respectively (m 2 ); the coefficients a i , b i , c i , d i , e i , and f i represent the respective contri- butions of the five functional zones to the overall carbon emis- sions; ε i is the error term.
− 0 : 0049 × φ 2
(11)
β
+ 1 : 088 × φ L pv × sin β opt ) tan ( 66 : 55 ∘ − φ ) (
opt =
(12)
d s = L pv × cos β
opt +
where β s is the spacing between PV panels (m), φ is the latitude of the plant location ( ◦ ), and L pv is the length of the PV panel (m). We then calculated the roof-mounted PV power generation E pv (kWh) using equations (13) and (14): A pv = A a × 1 d s (13) opt is the optimal radiation angle ( ◦ ), d
2.5. Model evaluation metrics
To evaluate the classification model's performance, we used accuracy, precision, recall, and the F1 score as evaluation metrics, which are denoted by U , P , R , and F 1 , respectively. Their formula- tions are given in equations (4) – (7): U = N TP + N NP N TP + N TN + N FP + N FN × 100% (4) P = N TP N TP + N FP × 100% (5) R = N TP N TP + N FP × 100% (6)
1 − F s 3 : 6
(14)
E pv = θ × A pv × H
δ ×
T ×
where A pv represents the area of the rooftop PV panels (m 2 ), A a is the total area of the rooftop (m 2 ), H T is the total solar radiation intensity at the plant location (MJ m − 2 year − 1 ), θ is the efficiency of PV modules, and F s is the shading coefficient. δ is the performance ratio (set to 0.8 following Wang et al. [41]). We set θ = 15% and F s = 0.05, following Wang et al. [42]. MJ was converted to kWh by dividing by 3.6. Finally, to calculate CER ( C pv ), we used the following equation: C pv = ( Q coal − Q pv ) × E pv (15) where Q coal and Q pv are the life cycle for coal-fired and PV power generation (g CO 2 kWh − 1 ), respectively. According to IPCC [43], we used Q coal = 950 g CO 2 kWh − 1 and Q pv = 50 g CO 2 kWh − 1 . 2.7. GSA method GSA was conducted using a variance-based approach that al- lows for correlated input parameters. The model output is expressed as: Y = g ( X 1 ; X 2 ; … ; X n ) (16) where Y is the model output and X 1 ; X 2 ; … ; X n represent the un- certain input parameters. Assuming the model is executed M times, the sample variance is calculated as follows:
2 × P × R P + R
(7)
F
1 =
where N
positives) are true examples, N
negatives)
TP (true
TN (true
positives) are false positive
are true counterexamples, N
FP (false
examples, and N negatives) are false negative examples. For the semantic segmentation model based on remote-sensing images, we employed the intersection over union ( IoU ) to measure the similarity between the model’s segmentation results and the true labels (equation (8)). FN (false
S S
G k G k
k ∩ k ∪
IoU
(8)
k =
In this equation, IoU k denotes the intersection over union for class k , S k is the pixel area predicted by the model, and G k is the annotated pixel area. For the carbon emission regression model, we employed the coefficient of determination ( R 2 , equation (9)) and the mean ab- solute percentage error ( MAPE , equation (10)) as evaluation metrics.
∑ n i = 1
2
̂ y
(
2
( y
M
i −
i )
Y )
1 M ∑
Y
Var ( Y ) =
(17)
R 2
j −
= 1 −
(9)
∑ n i = 1
j = 1
2
y )
( y
i −
where Y j is the result of the j -th model run, and Y represents the mean value obtained from M model runs. Finally, we used equations (18) and (19) to calculate the first- order and total-order Kucherenko sensitivity indices [44]:
⃒⃒⃒ y
1 n ∑ n i = 1⃒
̂ y i
i −
MAPE =
× 100%
(10)
y i⃒⃒⃒⃒
In these equations, y i , ̂ y i , and y refer to the actual, predicted, and average carbon emissions of the i -th plant, respectively, and n is the total number of samples.
Var ( Y | X i ) Var ( Y )
(18)
S
i =
Var ( Y | X Var ( Y )
∼ i )
(19)
S
1 −
T ; i =
2.6. Assessment of CER potential
where S i is the first-order Kucherenko sensitivity index of input the total output variance while allowing for correlated inputs. The total- effect Kucherenko sensitivity index S T ; i denotes the total-effect Kucherenko sensitivity index reflects the overall influence of X i paramete, which quantifies the contribution of X i to
To assess the CER potential of rooftop PV power generation for existing PPPs, we assumed that all other built-up areas were available for PV panel installation. For the modeling calculations, we followed the approach described by Wang et al. [41] (equations (11) and (12)):
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