Which year shows the highest change in revenue obtained from journals over the…

Concept — year-on-year change. In a data table, the "change over the previous year" attached to a row is that row’s value minus the value in the row immediately above it. The word "change" is unsigned: it compares the SIZE of those differences, so a fall counts exactly as much as a rise of the same size. A row that has no preceding row inside the table has no year-on-year change defined for it at all.

Application. The Journals column of the table reads ₹45 lakh (2020), ₹47 lakh (2021), ₹40 lakh (2022) and ₹40 lakh (2023). Take each row against the row above it:

1. 2020 — this is the opening row of the table. The 2019 revenue that a year-on-year difference would need is not supplied, so no change can be formed for 2020.

2. 2021 — 47 − 45 = +2, so the size of the change is 2 (a rise of ₹2 lakh).

3. 2022 — 40 − 47 = −7, so the size of the change is 7 (a fall of ₹7 lakh).

4. 2023 — 40 − 40 = 0, so the size of the change is 0 (journal revenue holds steady).

Cross-check. Laying the four rows side by side makes the ranking explicit:

Ranking the sizes gives 7 > 2 > 0, so the largest year-on-year movement in journal revenue is the ₹7 lakh drop recorded in 2022.

Contrast — why the near-misses fail. Two traps are built into this item. First, direction: if the stem had asked for the highest INCREASE, the answer would be 2021 (+₹2 lakh); because it asks for the highest CHANGE, the unsigned ₹7 lakh fall wins. Second, the opening row: 2020 is simply the first year printed, which tempts a reader who scans the column top-down instead of forming differences — but a "change over the previous year" question can never be answered by the first year in the data, since the comparison year sits outside the table. And 2023 repeats the previous year’s ₹40 lakh exactly, giving it no change at all.

Result. The highest change in journal revenue over the previous year occurs in 2022.