Conservation and Avoided Deforestation: Evidence from Protected Areas of Tanzania

Abstract

Tanzania dedicates a substantial proportion (38%) of its territory to conservation, with a large number of Protected Areas (PAs) managed under various regimes. Nevertheless, the country still experiences high rates of deforestation, which threaten the ecological integrity and socio-economic benefits of its forests. We utilized the Global Forest Change Dataset (2012–2022) and implemented a Propensity Score Matching (PSM) approach followed by a series of binomial logit regression modeling. Our objectives were to evaluate (1) the likelihood of PAs in avoiding deforestation compared with unprotected forest landscapes, (2) the variability in effectiveness among the different PA management regimes in avoiding deforestation, (3) evidence of leakage, defined here as the displacement of deforestation beyond PA boundaries as a result of protection inside PAs. Our findings reveal that, despite ongoing deforestation within and outside of PAs, conservation efforts are, on average, three times more likely to avoid deforestation compared with unprotected landscapes. However, the effectiveness of avoiding deforestation significantly varies among the different management regimes. National Parks and Game Reserves are nearly ten times more successful in avoiding deforestation, likely because of the stringent set of regulations and availability of resources for implementation. Conversely, Nature Forest Reserves, Game Controlled Areas, and Forest Reserves are, on average, only twice as likely to avoid deforestation, indicating substantial room for improvement. We found little evidence of the overall leakage as a consequence of protection. These results highlight the mixed success of Tanzania’s conservation efforts, suggesting opportunities to enhance the effectiveness of many less protected PAs. We conclude by proposing potential strategic pathways to enhance further the climate and ecosystem benefits of conservation in Tanzania.

1. Introduction

Deforestation in the tropics, driven mainly by agricultural expansion, continues at an alarming rate, threatening the ecological, climate, and cultural values of natural ecosystems [1]. On the other hand, conservation has long been recognized for the critical roles it plays in protecting the ecological and cultural values of natural ecosystems [2,3], mitigating climate change through carbon sequestration, and promoting biodiversity conservation [4,5]. The urgency of combating biodiversity loss was highlighted during the Conference of the Parties, COP 15 (2022), on the Convention on Biological Diversity (CBD). The conference emphasized the need for well-managed and ecologically representative, well-connected, and equitably governed Protected Areas (PAs), advocating for the conservation of at least 30% of the world’s terrestrial, inland water, and coastal and marine areas by 2030 [3]. In response, nearly all countries around the world pledged to set aside almost one-third of their land and marine territories for conservation. This pledge aligns well with Goal 15 of the UN Sustainable Development Goals (SDGs) [6], which promotes the sustainable use of terrestrial ecosystems and aims to combat desertification and halt biodiversity loss. To meet these ambitious targets, a comprehensive understanding of the effectiveness of conservation measures is essential. Such knowledge could enhance a country’s ability to improve existing conservation strategies and expand protected areas.

The United Republic of Tanzania, referred to as Tanzania henceforth in this article, is widely recognized for its nature conservation efforts. According to the World Database on Protected Areas (WDPAs) [2], Tanzania has already devoted over a third (38%) of its geographical area to conservation, further underscoring its leading role in nature and forest conservation. The nation’s commitment to conservation traces back to the late 19th century and continues to date with the establishment and management of hundreds of Protected Areas (PAs). This effort has transformed Tanzania into a major safari destination with some of the most recognized world heritage conservation areas, such as the Serengeti and Ngorongoro national parks. Initially, PA management in Tanzania aimed at establishing forest reserves for resource production (timber and charcoal), natural resources protection (e.g., water catchments), and implementing plantation forestry with exotic tree species [7]. Over time, the objectives were expanded to include tourism, conservation of biodiversity, and conservation of natural and cultural landscapes [8]. Currently, Tanzania’s PAs managed by the central government fall under six management regimes, each representing a set of strategies for forest and forest resources governance. Accordingly, (i) Forest Reserves allow selective logging and collection of non-timber forest products by authorized individuals; (ii) Game Controlled Areas allow limited subsistence farming, livestock keeping, and wildlife viewing; (iii) Game Reserves allow wildlife viewing and limited hunting of wildlife; (iv) National Parks, primarily for wildlife conservation and tourism, prohibit any resource extraction and (v) Nature Forest Reserves, primarily aimed at forest and biodiversity conservation, also prohibit any resource extraction, and (vi) Forest Plantations allow legal harvesting of wood products and encourage replanting. Collectively, these PAs cover a range of Tanzanian forests, vegetation types and landscapes, including primary forests, tropical high forests, miombo woodlands, mangrove forests, wetlands, grasslands, and other woodlands.

While Tanzanian PAs have sequestered much of the forest carbon in the country [9,10], a significant portion of the population depends on natural forests for fuelwood and charcoal [11]. Furthermore, agricultural and mining expansions continued to encroach into forested lands [12]. These factors have resulted in one of the highest deforestation rates globally, with an estimated annual forest loss of 470,000 hectares during the years 2002 to 2021 [13]. Even with strict protection, conservation efforts may lead to leakage, defined generally as a displacement of deforestation beyond PA boundaries, potentially undermining conservation efforts [14,15,16,17,18,19,20]. Therefore, to ensure the continued role of forests, it is crucial to implement a more robust measure that addresses not only deforestation inside PAs, but also prevents leakage, a spillover from a deforestation pressure inside PAs into the unprotected buffer areas.

Tanzania’s extensive and diverse network of PAs offers a compelling case for continued investigation into their effectiveness in reducing deforestation. Several previous studies have provided the basis and informed our approach to exploring conservation in Tanzania from various perspectives. For instance, Green et al. [21] presented predictors of forest and woodland conversion in the Eastern Arc mountains of Tanzania. Liang et al. [9] assessed carbon stocks and ecological structures in protected areas. Ract et al. [7] evaluated the management effectiveness of the Nature Forest Reserves of Tanzania, and Gizachew et al. [10] analyzed deforestation patterns within and outside of Tanzanian PAs between the period 2002 and 2013. Given the dynamic nature of deforestation and the diverse strategies employed in PA management regimes, it is crucial to regularly monitor conservation outcomes. Understanding PAs management effectiveness is also vital for management authorities, policymakers, and practitioners. Such knowledge may enable them to reinforce successful strategies and re-evaluate approaches where effectiveness is lacking, and ultimately seek for additional resources and political support for conservation efforts.

In this study, we drew on spatial data from protected areas in Tanzania, a spatio-temporal remote sensing dataset of forest cover and loss for the period 2012–2022, and data on a range of biophysical and demographic covariates relevant to conservation and deforestation. Our specific objectives are to assess and evaluate (1) the likelihood of Protected Areas (PAs) in avoiding deforestation compared with unprotected forest landscapes, (2) the variabilities in effectiveness among the different PA management regimes in avoiding deforestation, and (3) evidence of leakage, the displacement of deforestation pressure beyond PA boundaries due to protection.

2. Methodology

2.1. Study Area

Data on the Protected Areas (PAs) of Tanzania, including spatial boundaries (Polygons of PAs) and management regimes, were obtained from the World Database for Protected Areas (WDPA). The PA database is managed by the United Nations Environment Programme, World Conservation Monitoring Centre (UNEP-WCMC), with support from IUCN and its World Commission on Protected Areas (WCPA). We focused on PAs that were established prior to 2012 and have forests by 2012. These are PAs managed by the central government of Tanzania under six management regimes (

Table 1. Managing authorities, number, and area size of the six PA Management Regimes of the central government of Tanzania. Source: WDPA [2].

PA Managing Authority	PA Management Regime	Number of PAs	Area (1000 ha)
Tanzania Forest Services	Nature Forest Reserves (NFR)	8	73
	Forest Reserves (FR)	657	14,240
	Forest Plantations (FP)	23	5961
Tanzania Wildlife Management Authority	Game Controlled Areas (GCA)	18	8256
	Game Reserves (GR)	17	4848
Tanzania National Parks Authority	National Parks (NP)	17	330
Total		740	33,707

) as National Parks (NP), Game Reserves (GR), Game Controlled Areas (GCA), Nature Forest Reserves (NFR), Forest Reserves (FR), or Forest Plantations (FP). Figure 1 presents the locations of these PAs within Tanzania. We did not include other PAs, such as village forest reserves and wildlife management areas, due to inadequate spatial data or lack of accurate boundary polygons. The database consisted of 740 PAs covering a total area of over 33.7 million ha. The numbers at the sources of PA Management Authorities of Tanzania might vary; for instance, some Forest Reserves have been recently reclassified as Nature Forest Reserves. Unprotected buffer areas of 5 Km surrounding each PA were constructed and added to the database as Buffer Areas. The number and area estimate of PAs in Table 1 and the Management regimes represent data collected from WDPA through the Protected Planet website (https://www.protectedplanet.net/) accessed on 5 January 2023.

2.2. Data and Sampling

2.2.1. Spatial Data on Forests and Deforestation

We obtained the Global Forest Change Dataset (GFCD) (30m resolution) for the period 2012–2022 [22], covering the entire area of Tanzania. For spatial data on the forests of Tanzania, we extracted pixels that were forested in 2012 from the tree cover loss data. In order to remain as close as possible to the official definition of forests in Tanzania [13], we set a threshold of 10% tree canopy cover in the GFCD. We then extracted pixels that experienced tree cover loss during our decade of interest (2012–2022). We used “tree cover loss” in the GFCD as a proxy for deforestation, reflecting Tanzania’s context where tree cover loss is often permanent. However, forest plantations do not fulfill this definition. Managed for timber and wood by the Tanzanian Forest Service, forest plantations experience legal harvesting, leading to temporary tree cover loss followed by replanting. To avoid the possibility of misclassification of such practices as deforestation, we excluded forest plantations (23 PAs) from the final analysis.

2.2.2. Sampling and Sample Plots

We used a stratified random sampling approach by overlaying the forested area map of Tanzania by the year 2012, which is obtained from the GFCD data (described in Section 2.2.1) and the area polygons of 740 PAs obtained from WDPA. This established 741 strata, including all the 740 PAs as individual strata and the unprotected forests as an additional stratum. We approximated the sample size at an intensity of roughly one point per square kilometer of forested area. Given the requirements of Propensity Score Matching (PSM), we needed a larger pool of potential control plots (unprotected areas) compared with treatment plots (protected areas). We aimed to obtain nearly double the number of sample plots within unprotected areas. This strategy was designed to increase the chances of finding suitable matches for selected plots within Protected Areas from the pool of unprotected plots. We distributed the sample plots proportionally across the strata, ensuring a minimum of five randomly selected plots per PA. This approach produced a total of 457,342 plots, of which 303,339 fall within the unprotected forests and the remaining 154,003 within the 740 PAs. We controlled for PA size by sampling proportional to the forest area in 2012 in each PA while keeping a minimum of five plots per PA. Data, sampling and subsequent analyses are summarized in Figure 2.

2.2.3. Covariate Data for Propensity Score Matching (PSM)

We collected data sets for seven covariates from various databases (

Table 2. Summary of spatial data of covariates used in the Propensity Score Matching (PSM).

Covariate	Description	Source
Elevation	Elevation in meters above mean sea level	ASTER GDEM V2 [24]
Slope	Slope (%)
Agricultural areas	Polygons of Farmlands in Tanzania for the period near 2012	[26]
Water bodies	Shape-file of inland water bodies of Tanzania	[26]
Towns	Towns from The Multipurpose Africover Database for the Environmental Resources produced by the Food and Agriculture Organization of the United Nations (FAO).	[27]
Roads	Center line of principal roads extracted from Open Street Map.	[28]
International boundaries	Shape polygon of Tanzania Administrative level	[29]

). These covariates are theoretically assumed and practically known to affect the establishment of a PA and are directly associated with deforestation inside and outside of PAs [23]. While several other covariates may be relevant, we selected a few for which reliable spatial data were available. For each sample plot within PAs (the treatment group) and outside of PAs (the control group), we extracted the slope and elevation values from ASTER GDEM V2 [24]. We computed the Euclidean distance [25] from each plot to the nearest polygons of agricultural areas, water bodies, towns, roads, and international boundaries. These spatial analyses were performed in ArcGIS 10.

2.2.4. Data Limitations

In this study, we relied on the Global Forest Change Dataset (GFCD) (30 m resolution) for the period 2012–2022 [22], a list of data sources for covariates and WDPA for boundary polygons for PAs [2], each of which has inherent accuracy limitations. In the absence of reliable validation data, our validation was primarily based on the visual interpretation of figures and our local expertise. Although these datasets have been frequently utilized in similar contexts, care should be taken when interpreting results, especially when quantifying the deforestation of forest areas or smaller Protected Areas. Furthermore, this study utilized forest cover loss data to approximate deforestation. Data on ecosystem services and the economic benefits and costs associated with individual Protected Areas (PAs) or management regimes—factors that might impact the results—were not readily available for the study period (2012–2022).

2.3. Data Analysis

2.3.1. Propensity Score Matching

Propensity Score Matching (PSM) is a statistical approach designed to estimate the effect of treatment by balancing the treatment and control groups on a set of observed baseline covariates [30]. The PSM has long been a popular tool in the areas of social sciences, economics, public health, and epidemiology [30]. Recently, PSM has been increasingly applied to evaluate the effectiveness of conservation efforts by creating balanced, matched pairs between treatment (protected) and control (unprotected) groups [9,31,32]. The assumption in applying PSM to evaluate the effectiveness of protection or a conservation regime is that after controlling for the confounding factors, such as variables associated with biophysical, demographic, or socio-economic factors, any remaining differences in deforestation rates between the protected and unprotected forests can be attributed to the protection or the lack of it [23,32].

In this study, we conducted preliminary tests on two Propensity Score Matching (PSM) approaches [33]. (i) Matching with replacement, where multiple plots in the unprotected (control) group could be matched to a single plot in the protected (treatment) group, and (ii) Greedy matching, which paired each plot in the protected group (treatment) with a unique plot in the unprotected (control group). For both approaches, we used the most common matching strategy—the nearest neighbor matching (K = 1) with a caliper of 0.2, meaning the maximum distance between matched pairs fell within 0.2 standard deviations of the estimated propensity score. We subsequently assessed the performance of the two matching approaches using three commonly employed statistical measures that assess the balance of covariates between the protected and unprotected groups [33]. These are the Standardized Mean Difference (SMD), Percent Bias Reduction (PBR), and Variance Ratio (VR). The PSM with greedy approach and with replacement resulted in comparable Standardized Mean Difference (SMD = 0.08, vs. 0.06) and Variance Ratio (VR) (VR = 1.2 vs. 1.3), respectively. However, the PBR was higher for the greedy approach (89%) compared with PSM with replacement (50%). We, therefore, used greedy matching to generate paired plots between the protected and unprotected groups.

PSM prior to analysis of PA and PA management effectiveness: The PSM was intended to generate paired data between plots within PAs (the treatment group) and those located outside PAs (the control group). Prior to the PSM, we excluded Forest Plantations (23 PAs) and a 5 km buffer area surrounding each PA from our data set. This reduced the total number of plots to 385,987. We then employed the PSM, which yielded 137,062 paired plots (n = 274,124), both protected and unprotected, for the subsequent analysis.

PSM prior to analysis of Leakage: This aimed to generate paired plots for further analysis of evidence of leakage. This involved pairing two groups of plots, namely (a) plots within buffer areas, areas within a 5 km distance from each PA boundary, and (b) plots within areas outside PAs, excluding buffer areas. The PSM for leakage analysis resulted in 70,785 paired plots (n = 141,570).

The PSM approach reduced spatial biases between sample plots inside PAs (treatment group) and sample plots outside PAs (control groups) by incorporating location, covariates (elevation, slope, and various distances). However, it is important to note that PSM did not explicitly address spatial autocorrelation for sample plots within the PAs. Alternative procedures that account for spatial autocorrelation patterns in the treatment group during PSM analysis or when computing the odd ratios may be needed in future analysis.

For the final PSM in all the cases, we used the PSMatch procedure in SAS 9.4 [34]. The same result can be obtained using the “MatchIt” procedure in the R package “MatchIt.” However, the matching process in R requires a longer computation time, especially for multiple PSM runs.

2.3.2. Logistic Modeling, Log Odds of Avoided Deforestation, and the Odds Ratios

After generating the balanced, paired plot data using the PSM procedure (Section 2.3.1), we fitted a series of binomial logistic regression models using maximum likelihood for estimation of model coefficients (Equations (1)–(3)) followed by computation of the Odds Ratios (ORs). For the specification of the generalized binomial models to estimate coefficients for the log odds of avoided deforestation, we followed Wooldridge [35].

(i)

A model for an overall protection effect: To assess the overall protection effect, we fitted a binomial logit model (Equation (1)) with a binary response variable—Avoided Deforestation (AD), and a binary predictor representing Treatment (T), indicating whether a forest area was Protected or Unprotected. The purpose is to estimate the change in the odds of avoiding deforestation due to protection compared with unprotected forests.

The binomial logit model was specified as:

Logit (P(AD = 1)) = β₀ + β₁(T) + ε

(1)

where Logit (P(AD = 1) is the log odds of Avoided Deforestation, β₀ is the intercept, representing the log odds of Avoided Deforestation for the control groups (unprotected forests). β₁: the slope coefficient for Protection (T), representing the change in the log odds of Avoided Deforestation (AD) due to protection. The coefficient β₁ was used for the calculation of the Odds Ratio (OR), which quantifies the effect of the treatment (protection) on the odds of avoided deforestation compared with unprotected forests, and ε: is the error term, which accounts for the variation in the log odds not explained by protection (T).

(ii)

Effectiveness of the PA management regimes compared with no protection: To evaluate the effectiveness of the PA management regimes compared with unprotected forest areas, we specified a logit model (Equation (2)). In Equation (2), the response variable was Avoided Deforestation (AD), with a binary outcome, i.e., (AD = 1 if deforestation was avoided; AD = 0 deforestation occurred). The predictor variable was categorical with six levels, representing the five management regimes (NP, GR, FR, NFR, GCA) (see Table 1) and the reference category Unprotected Forests (UF).

The model was specified as:

Logit (P (AD = 1)) = β₀ + β₁(NP) + β₂(GR) + β₃(FR) + β₄(NFR) + β₅(GCA) + ε

(2)

where Logit (P(AD = 1)) is the log odds of avoided deforestation (AD), β₀ is the intercept representing the log odds of avoided deforestation for the unprotected forests. The coefficients β₁, β₂, β₃, β₄, and β₅ are the changes in the log odds of avoided deforestation associated with the management regime (NP, GR, FR, NFR, and GCA, respectively), compared with unprotected forests, and ε represents the error term—accounting for the unexplained variation in the log odds of avoided deforestation that is not accounted for by the management regimes.

(iii)

Comparing the effectiveness of the five PA management regimes: After evaluating the effectiveness of each management regime compared with the unprotected forests (UF), we further compared the effectiveness between any two management regimes, using the odds ratio. First, we fitted five additional logit models similar to Equation 1, but each with a different management regime (NP, GR, FR, NFR, and GCA) as the reference category. The resulting coefficients allowed us to calculate the odds ratio between any two management regimes to compare their effectiveness in avoiding deforestation.

(iv)

Assessment of Leakage: To assess evidence of leakage, we fitted a logit model (Equation (3)) with a binary response variable (D = 1 if Deforested, D = 0 if not deforested) and a binary predictor representing location (L = 1 if inside buffer areas, L = 0 if in the unprotected forest areas beyond the buffer). Equation (3) estimated the log odds of deforestation within the buffer areas compared with unprotected areas outside the buffer areas.

Logit(P(D = 1)) = β₀ + β₁(L) + ε

(3)

where Logit(P(D = 1)) is the log odds of Deforestation (D), β₀: Intercept, representing the log odds for locations outside buffer areas; β₁: the slope coefficient, captures the effect of being within the buffer zone on the log odds of deforestation. β₁ was used for the calculation of the Odds Ratio (OR) to compare the effects of location inside the buffer compared with the unprotected areas outside the buffer areas.

(v)

Computing and interpreting the Odds Ratios (OR). Equations (1)–(3) above estimate the coefficients corresponding to the log odds of avoided deforestation. However, for a more intuitive comparison of the effectiveness of protection against no protection, a quantitative comparison of the different management regimes, or assessing leakage, we calculated the odds ratio (OR). The OR is computed as OR = exp(βi), where βi is the slope coefficient for the predictor variable (s) in each equation. For interpretation of the OR values, for instance, an OR > 1 indicates higher odds of avoided deforestation compared with the reference category. The computation of ORs also generates a Wald Confidence Interval (WCI). The WCIs were used to assess the statistical significance of each OR. Accordingly, confidence intervals that do not include 1 were considered statistically significant.

3. Results

3.1. The Overall Effect of Protection

Despite ongoing deforestation within PAs, our analysis revealed a statistically significant positive effect of protected areas (PAs) in reducing deforestation compared with unprotected forest landscapes. PAs exhibited an average Odds Ratio (OR) of 2.79 (95% Confidence Interval (CI): 2.58–3.02), suggesting a nearly threefold higher likelihood of avoiding deforestation within PAs compared with unprotected areas.

3.2. Effectiveness of PA Management Regimes Compared with Unprotected Areas

Table 3. The Odds Ratio Estimates (Protected vs Unprotected) for each management regime.

Management Regime	Odds Ratio (Protected vs. Unprotected)	Lower 95% CI	Upper 95% CI
National Parks	8.54 *	7.11	10.32
Game Reserves	9.76 *	8.76	11.16
Nature Forest Reserves	2.05 *	1.53	2.76
Game Controlled Areas	2.28 *	2.10	2.53
Forest Reserves	1.58 *	1.51	1.66

Asterisks (*) denote odds ratios significantly different from 1, indicating that the likelihood of Avoided deforestation in the treated (protected) area significantly differs from the control (unprotected) area. Odds ratios greater than 1 indicate higher likelihood. CIs that do not include 1 are significant.

presents the odds ratio as well as the corresponding Confidence Intervals for effectiveness in avoiding deforestation of each of the five PA management regimes compared with no protection. Our assessment of the effectiveness of the management regimes revealed that all five PA management regimes were significantly effective in reducing deforestation compared with unprotected areas, with National Parks (NPs) and Game Reserves (GRs) emerging as the most effective regimes, demonstrating the highest Odds Ratio of avoiding deforestation. Nature Forest Reserves, Game Controlled Areas, and Forest Reserves also showed positive results, albeit with much lower odds of avoiding deforestation compared with unprotected areas (Table 3).

3.3. Differences between Pairs of Management Regimes

In assessing differences between management regimes, we conducted pairwise comparisons (in odds ratios) between management regimes. Our analysis revealed two distinct groups. National Parks (NPs) and Game Reserves (GRs) showed statistically similar effectiveness in reducing deforestation, while Game Controlled Areas, Nature Forest Reserves, and Forest Reserves showed slight variations among them but generally achieved comparable effectiveness.

Table 4. Odds Ratios and statistical significance between pairs of the Different Management regimes.

		Reference Management Regime
Management Regime		Game Reserves	National Parks	Game Controlled Areas	Nature Forest Reserves	Forest Reserves
Game Reserves		1	1.14 a	4.28 *	4.77 *	6.18 *
National Parks		0.87 a	1	3.75 *	4.17 *	5.40 *
Game Controlled Areas		0.23 *	0.27 *	1	1.11 b	1.44 *
Nature Forest Reserves		0.21 *	0.24 *	0.90 b	1	1.30 c
Forest Reserves		0.16 *	0.18 *	0.69 *	0.77 c	1

Numbers in each cell are Odds Ratios between a Management Regime and a Reference Management Regime. Statistical significance (*) are odds ratios compared between pairs of PA management regimes. Odds Ratios with the same lowercase letters are not significantly different.

presents the odds ratios comparing the likelihood of avoiding deforestation between pairs of management regimes. Each cell represents the odds ratio between the pairs of management regimes. For example, the odds of avoiding deforestation in National Parks (row) compared with Nature Forest Reserves (column) is 4.17. An odds ratio greater than 1 indicates a higher likelihood of the row management regime avoiding deforestation compared with the column management regime. Conversely, an odds ratio of less than 1 suggests a lower likelihood. Asterisks (*) denote statistically significant differences at the 95% Wald Confidence Interval. Ranking between management regimes in order of significance in avoiding deforestation is consequently (GR = NP) > (GCA = NFR ≥ FR).

3.4. Evidence of Leakage

To assess evidence of leakage, we compared the likelihood of deforestation in buffer zones surrounding protected areas to unprotected forest landscapes beyond these zones. We estimated an odds ratio of 1.03 (confidence interval: 0.983–1.069) between the two zones. This indicated that the buffer zones and the unprotected areas beyond the buffer zones are statistically identical, thereby providing overall evidence of ‘no leakage’ attributable to protection during the period 2012–2022.

4. Discussion

4.1. Conservation Outcomes Are Positive Despite Continued Deforestation

Our evaluation of a decade-long (2012–2022) forest loss dataset within and outside the Protected Areas (PAs) of Tanzania revealed persisting deforestation within a considerable number of PAs, particularly Forest Reserves. However, the annual rate decreased from 0.8% in the previous decade (2002–2013) [10] to 0.2% (2012–2022). Figure 3 illustrates two Forest Reserves that lost over 20% of their forest cover during the 2012–2020 period. Despite this, the estimated odds ratio between protected and unprotected forest landscapes suggests that without conservation efforts through the five management regimes, deforestation within PAs could have been theoretically three times higher. Our descriptive statistics further corroborate this result, indicating a 2% forest cover loss within PAs compared with a 5% loss in unprotected areas over the period. We also noted significant variation in deforestation rates among PAs. For instance, while 15% of the PAs, primarily national Parks, Game reserves, and smaller Forest Reserves in mangrove forests, maintained their forest cover, a small subset (5%), mainly Forest Reserves, lost more than a quarter of their forest cover during the same period.

Our findings on the positive roles of conservation in reducing deforestation rates (e.g., Figure 4) within Tanzanian PAs align with broader observations across tropical forests. For instance, a global meta-analysis [36] of 186 studies found improved biodiversity outcomes in two-thirds of the conservation interventions, and [31] documented a significant increase in above-ground carbon stocks within African PAs compared with unprotected areas. Fritz, Bayas, See, Schepaschenko, Hofhansl, Jung, Duerauer, Georgieva, Danylo, Lesiv and McCallum [37] showed that the deforestation rate in PAs was lower than the continental averages in both Latin America and Africa, and Wade, Austin, Cajka, Lapidus, Everett, Galperin, Maynard and Sobel [38] concluded from A Global Assessment of Deforestation that forest loss rates in PAs were less than in non-protected forests. Reinforcing this point, Yang, Viña, Winkler, Chung, Dou, Wang, Zhang, Tang, Connor, Zhao and Liu [32] found that over 70% of Chinese PAs effectively reduced deforestation. Collectively, these research studies, including our study in Tanzania, underscores the critical role of conservation in mitigating deforestation, with positive implications for both carbon storage and biodiversity preservation.

4.2. Management Regimes Presented Variable Effectiveness

Our analysis of the effectiveness across the five management regimes resulted in two distinct groups. Group 1, comprising National Parks and Game Reserves, demonstrated the highest effectiveness in reducing deforestation (Table 3, results section and Figure 3. For example). A pairwise comparison between the two management regimes—National Parks and Game Reserves—revealed no significant differences in their likelihood of avoiding deforestation (Table 4). This result is expected as the two management regimes enforce stringent regulations prohibiting the extraction of timber or other forest products within PA boundaries and granting access only for research, education, and nature-based tourism [39]. This finding also aligns well with previous studies [10,40], reinforcing evidence that rigorous policy implementation in National Parks and Game Reserves contributed to effectively reducing deforestation. Group 2, which comprises Nature Forest Reserves, Game Controlled Areas, and Forest Reserves, demonstrated moderate to low effectiveness in reducing deforestation compared with unprotected areas (see Table 3 and Figure 5 for example). A notable outcome is that, despite stricter regulations prohibiting most human activities, Nature Forest Reserves exhibited an effectiveness similar to Game Controlled Areas and Forest Reserves (Table 4). The Forest Policy of 1998 and the Forest Act of 2002 [41] granted Nature Forest Reserves stronger protection comparable to National Parks and Game Reserves. In a previous study [10], Nature Forest Reserves were reported to be the most successful in retaining the largest proportions of their forest cover during the period 2002–2013. While a closer look is needed, enforcement challenges or lack of resources during the period 2012–2020 might explain the observed lower effectiveness compared with, for instance, National Parks and Game Reserves.

Forest Reserves, which make up 84% of the Protected Areas (PAs) in Tanzania, are distributed widely across the country. They exhibited a broad spectrum of effectiveness in reducing deforestation rates. For instance, 5% of the PAs managed as Forest Reserves lost over 20% of their forest cover, while over 40% showed no or minimal forest loss. The most effective Forest Reserves are generally smaller and primarily located along the mangrove forests and the Usambara Mountains in northeastern Tanzania. This is consistent with previous studies in PAs of Tanzania. For instance, Liang, Gonzalez-Roglich, Roehrdanz, Tabor, Zvoleff, Leitold, Silva, Fatoyinbo, Hansen and Duncanson [9] suggested that smaller PAs may be more effective for conservation, and Gizachew, Rizzi, Shirima and Zahabu [10] found that larger PAs which represent over 75% of the total protected area in Tanzania, accounted for more than 90% of the total deforestation during the period 2002–2013. Given the extensive number and widespread geographical distribution of PAs managed as Forest Reserves, it is challenging to generalize the effectiveness of Forest Reserves as a single management regime. Instead, the significant variation in effectiveness underscores the importance of investigating the factors that influence the effectiveness of individual Forest Reserves.

4.3. Limited Evidence of Deforestation Displacement

Leakage, a potentially negative outcome of protection, has been documented in previous studies as potentially offsetting conservation gains, e.g., [14,42,43]. Consequently, leakage is a crucial factor to consider when assessing PA effectiveness. Our study, however, found minimal overall evidence of leakage, as indicated by the odds ratio close to 1 between buffer zones and unprotected areas beyond the buffer zones. Furthermore, comparable similar forest cover loss during the decade was observed within buffers (4.4%) and unprotected areas beyond the buffer zones (4%). Generally, there is a lack of management strategy for PA buffers in Tanzania, and this observation clearly indicates that PAs are facing high deforestation risks. Nevertheless, leakage risks might still exist at individual PAs, especially subsequent to the establishment of new PAs in areas with strong drivers of deforestation, such as illegal logging for charcoal production and agricultural expansion, as primary drivers of deforestation in Tanzania [12].

4.4. Implications to Climate and Biodiversity Objectives

Tanzania’s extensive PA network encompasses high-carbon forests such as the tropical rainforests of the Eastern Arc Mountains and the vast miombo woodlands across the northwestern, central, and southern parts of Tanzania [13]. These forests play a crucial role in climate change mitigation through carbon sequestration and storage. Our findings provide quantitative evidence demonstrating the effectiveness of PAs in reducing deforestation, a major source of emissions in Tanzania. This underscores the necessity for explicitly incorporating conservation as a policy tool in future climate action plans. These findings further strengthen the case for continued support for PAs, particularly in the context of Tanzania’s Nationally Determined Contribution (NDC) outlined in the Paris Agreement [44]. Furthermore, Tanzania’s adoption of REDD+ (Reducing Emissions from Deforestation and Forest Degradation) [45] reinforces the importance of conservation for mitigating climate change.

Tanzania is also a signatory to the UN Convention on Biological Diversity, which adopted ambitious global targets for 2030 aimed at halting and reversing biodiversity loss [3]. Tanzania’s PAs include networks of conservation areas along the Eastern Arc Mountains, identified as one of 25 global biodiversity hotspot areas [46]. Therefore, strengthening connectivity between PAs, improving management effectiveness in the less effective regimes, and expanding protection to include remaining biodiversity and high carbon hotspots could greatly boost Tanzania’s efforts to achieve its climate and biodiversity conservation goals.

5. Summary and Conclusions

Our study shows that Tanzanian conservation efforts generally reduced deforestation, albeit the continued deforestation within a number of PAs. Reduced deforestation as a conservation outcome varied across management regimes, with National Parks and Game Reserves being the most effective. These variations likely result from differences in enforcing conservation regulations and resource availability for enforcement across PAs or management regimes. The results further highlight the potential of conservation as a policy tool to combat deforestation, advance Tanzania’s climate and environmental objectives, and contribute to the global climate goals.

We conclude by proposing three potential strategic pathways to enhance or maintain the climate and ecosystem services benefits provided by conservation in Tanzania: (i) Prioritizing protection: focus on areas with high deforestation, particularly in larger Forest Reserves. (ii) Optimizing PA management: lessons should be drawn from successful management regimes, such as National Parks and Game Reserves, and consideration should be given to upgrading, for instance, Game Controlled Areas to Game Reserve status, which shared management authorities but show different results in effectiveness. (iii) Policy alignment: conservation has a great potential to contribute to national development and climate strategies, including the Nationally Determined Contribution (NDC), Sustainable Development Goals (SDGs), and REDD+.