According to the SHAP values of the driving factors (Fig. 11a), time-series features were essential for constructing the CatBoost GPP estimation model. Among them, NDVI demonstrated the most significant impact on the CatBoost GPP model, followed by FPAR. Notably, NDVI and FPAR can directly characterize the spatiotemporal heterogeneity of vegetation growth status and photosynthetic capacity77,78, thereby playing a dominant role in the model’s estimation process. As intra-annual changes in GPP vary significantly more than inter-annual changes, temporal features play a crucial role in the GPP estimation process. LST is an important environmental influencing factor in GPP estimation20, yet its influence on the model was lower than that of temporal feature. This is mainly because LST changes are not completely synchronous with vegetation growth rhythms, and they vary in impact across different growth stages79.
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However, there is a lack of carbon flux towers in Shanxi Province, and few studies have examined long-term GPP changes in this region. Furthermore, previous research mainly focused on the Yellow River Basin32 and LP33. For example, Zhang et al.33 explored the vulnerability of the LP ecosystem based on the GPP. Meanwhile, Gong et al.34 used a modified vegetation photosynthesis model to obtain a long-term series of GPP in the LP from 2001 to 2022 and investigate the impacts of different land use patterns and meteorological factors. Previous studies35,36,37 have revealed the spatiotemporal patterns of GPP in the LP and the Yellow River Basin, as well as their climatic and ecological driving mechanisms. Therefore, it is crucial to comprehensively quantify the impacts of factors such as climate, topography, human activities, and their interactions on the GPP in Shanxi Province.
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The regions where GPP was driven solely by temperature (4.96%) were scattered across the study area. Notably, in the southwestern and northwestern regions of Shanxi Province, GPP increases were closely related to human activities. Overall, the GPP in Shanxi Province noticeably improved from 2001 to 2022 (Fig. 8a). The percentage of areas characterized by significant improvement, slight improvement, slight degradation, and considerable degradation was 87%, 9%, 3%, and 1%, respectively. The areas of significant decline were mainly concentrated in densely populated areas of the cities. The largest proportion of the decline in GPP (8.1%) occurred in Taiyuan due to two main reasons.
- Table 2Emissions of the major SLCFs in 1750, 2019 and 2024 from a combination of CEDS and GFED.
- Meanwhile, approximately 98% of Linfen and Lvliang had significant increases in GPP.
- Positive readings may be less than before, but a positive Rate-of-Change still reflects a price increase, not a price decline.
- In this blog, you will learn about technical analysis and one of the most vital technical indicators called the Rate of Change Indicator.
- Traders may use trendlines on the ROC to identify peaks and troughs in momentum and look for divergences between momentum and price.
Calculation of the ROC Indicator
- As seen very often but not always ROC indicator is a powerful early signal either to buy or sell a stock.
- The rate of change indicator is a technical indicator that belongs to the momentum-based indicator family.
- A cross above zero signifies upside momentum is accelerating and indicates an opportunity to buy, as an uptrend may be starting.
- The work focuses on indicators related to heating of the climate system, building from greenhouse gas emissions towards estimates of human-induced warming and the remaining carbon budget for 1.5 °C and other policy-relevant temperature thresholds.
We used a Geodetector model to quantify the effects of different variables on the spatial distribution of GPP in Shanxi Province by performing factor and interaction detection. Furthermore, the EC flux data were excluded if less than 80% of the data were observed during the year, and stations with less than 2 years of observations were not used. Following screening, we used 14 EC ground stations, including eight ChinaFLUX sites and six FLUXNET2015 sites.
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Rate of Change (ROC) is used in technical analysis to identify trends, and spot reversals, and generate trading signals. ROC above its centerline signifies an uptrend with accelerating momentum. ROC below its centerline indicates a downtrend is gaining strength. The further ROC moves from the centerline, the stronger the trend becomes. ROC works best when used with other indicators like moving averages, trend lines or RSI.
Which markets work best with the ROC oscillator?
First, AR6 budgets were expressed from 2020 onwards, and approximately 200 Gt CO2 was emitted between 2020 and 2024. Second, we use updated physical models of non-CO2 forcing, which leads to an increased estimate of the importance of aerosols that are expected to decline with time in low emissions pathways (Rogelj et al., 2014; Rogelj and Lamboll, 2024). This decreased negative forcing from aerosols is expected to cause additional net non-CO2 warming because more non-CO2 GHG warming is being unmasked, and this decreases the RCB (Lamboll et al., 2023) by slightly over 100 Gt CO2. There was also a small reduction in the budget (about 10 Gt CO2) from using the newer AR6 scenario set.
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Price continues rising but ROC starts falling suggesting upside momentum is slowing and a downturn may be coming. Selecting overbought and oversold thresholds on the ROC can be a challenge. The methods shown above are only a few of the various ways to trade with the ROC indicator. Beyond those mentioned above, there are undoubtedly other ways to use the ROC. The ROC indicated that the price was above the zero-line once it forcefully broke out above the $29.58 resistance level or the upper range. The breakout appears to be continuing since the trajectory is also ascending higher.
The ROC indicator can be used to identify trends and price direction generally, but as an oscillator, it’s best used to generate overbought and oversold signals, particularly in sideways markets. Like most indicators, it’s best used alongside other technical analysis tools, especially those on the price chart, with one serving as confirmation of the other. 15 present a summary of the headline indicators from each section compared to those given in the AR6 assessment. AR6 assessed that global land precipitation has likely increased since the middle of the 20th century with a faster increase since the 1980s with large interannual variability and regional heterogeneity.
The downside with intra-day trading, especially stocks, is that the asset can rate of change indicator become volatile. This is when traders need to pay attention to the technical indicator’s settings and adjust it accordingly. For example, if the first few hours are very volatile for the security, then it is best to use one setting, and when the volatile subsides, switch over to another setting. In the second instance, while the moving averages were still bullish, the PROC indicator falls below the zero-line. The calculation for the price rate of change oscillator is, as the name suggests, the rate of change.
Yes, the Rate of Change (ROC) indicator is considered a leading indicator in some respects, but it also has some lagging characteristics. ROC has three main qualities that makes it a leading indicator. It detects shifts in momentum early and signals a potential trend reversal or continuation. The ROC generates false signals, especially in volatile or range-bound markets. It detects even small changes in momentum, and it signals a reversal that does not materialize. It is best used with other indicators to confirm signals and avoid false turns.
There are several variations of the ROC calculation depending on the charting software used, but they all follow the same basic approach and have the same interpretation. To calculate the ROC, compare today’s price to the price a certain number of days ago. We introduce people to the world of trading currencies, both fiat and crypto, through our non-drowsy educational content and tools. We’re also a community of traders that support each other on our daily trading journey. If ROC is positive but falling, buying pressure is decreasing, and the rise in price is slowing down.
Temperatures are based on an average of four datasets following AR6; see Sect. Concerning the second issue, there are varying conventions used to quantify CO2-LULUCF fluxes. Assessments also differ with respect to biomass fire emissions and to what extent components of these are treated as anthropogenic (Lamb et al., 2025). The update is based on methodologies assessed by the IPCC Sixth Assessment Report (AR6) of the physical science basis of climate change (Working Group One (WGI) report; IPCC, 2021a) as well as Chap. IPCC reports make a much wider assessment of the science and methodologies – we do not attempt to reproduce the comprehensive nature of these IPCC assessments here. We also do not consider adopting fundamentally different approaches to AR6.
